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Step-by-Step Guide: How to Craft a Strong Research Hypothesis

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A research hypothesis is a concise statement about the expected result of an experiment or project. In many ways, a research hypothesis represents the starting point for a scientific endeavor, as it establishes a tentative assumption that is eventually substantiated or falsified, ultimately improving our certainty about the subject investigated.   

To help you with this and ease the process, in this article, we discuss the purpose of research hypotheses and list the most essential qualities of a compelling hypothesis. Let’s find out!  

How to Craft a Research Hypothesis  

Crafting a research hypothesis begins with a comprehensive literature review to identify a knowledge gap in your field. Once you find a question or problem, come up with a possible answer or explanation, which becomes your hypothesis. Now think about the specific methods of experimentation that can prove or disprove the hypothesis, which ultimately lead to the results of the study.   

Enlisted below are some standard formats in which you can formulate a hypothesis¹ :  

• A hypothesis can use the if/then format when it seeks to explore the correlation between two variables in a study primarily.  

Example: If administered drug X, then patients will experience reduced fatigue from cancer treatment.  

• A hypothesis can adopt when X/then Y format when it primarily aims to expose a connection between two variables  

Example: When workers spend a significant portion of their waking hours in sedentary work , then they experience a greater frequency of digestive problems.  

• A hypothesis can also take the form of a direct statement.  

Example: Drug X and drug Y reduce the risk of cognitive decline through the same chemical pathways  

What are the Features of an Effective Hypothesis?  

Hypotheses in research need to satisfy specific criteria to be considered scientifically rigorous. Here are the most notable qualities of a strong hypothesis:  

• Testability: Ensure the hypothesis allows you to work towards observable and testable results.  
• Brevity and objectivity: Present your hypothesis as a brief statement and avoid wordiness.  
• Clarity and Relevance: The hypothesis should reflect a clear idea of what we know and what we expect to find out about a phenomenon and address the significant knowledge gap relevant to a field of study.   

Understanding Null and Alternative Hypotheses in Research  

There are two types of hypotheses used commonly in research that aid statistical analyses. These are known as the null hypothesis and the alternative hypothesis . A null hypothesis is a statement assumed to be factual in the initial phase of the study.   

For example, if a researcher is testing the efficacy of a new drug, then the null hypothesis will posit that the drug has no benefits compared to an inactive control or placebo . Suppose the data collected through a drug trial leads a researcher to reject the null hypothesis. In that case, it is considered to substantiate the alternative hypothesis in the above example, that the new drug provides benefits compared to the placebo.  

Let’s take a closer look at the null hypothesis and alternative hypothesis with two more examples:  

Null Hypothesis:  

The rate of decline in the number of species in habitat X in the last year is the same as in the last 100 years when controlled for all factors except the recent wildfires.  

In the next experiment, the researcher will experimentally reject this null hypothesis in order to confirm the following alternative hypothesis :  

The rate of decline in the number of species in habitat X in the last year is different from the rate of decline in the last 100 years when controlled for all factors other than the recent wildfires.  

In the pair of null and alternative hypotheses stated above, a statistical comparison of the rate of species decline over a century and the preceding year will help the research experimentally test the null hypothesis, helping to draw scientifically valid conclusions about two factors—wildfires and species decline.   

We also recommend that researchers pay attention to contextual echoes and connections when writing research hypotheses. Research hypotheses are often closely linked to the introduction ² , such as the context of the study, and can similarly influence the reader’s judgment of the relevance and validity of the research hypothesis.  

Seasoned experts, such as professionals at Elsevier Language Services, guide authors on how to best embed a hypothesis within an article so that it communicates relevance and credibility. Contact us if you want help in ensuring readers find your hypothesis robust and unbiased.  

References  

• Hypotheses – The University Writing Center. (n.d.). https://writingcenter.tamu.edu/writing-speaking-guides/hypotheses  
• Shaping the research question and hypothesis. (n.d.). Students. https://students.unimelb.edu.au/academic-skills/graduate-research-services/writing-thesis-sections-part-2/shaping-the-research-question-and-hypothesis  

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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How to Write a Hypothesis – Steps & Tips

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 26, 2023

What is a Research Hypothesis?

You can test a research statement with the help of experimental or theoretical research, known as a hypothesis.

If you want to find out the similarities, differences, and relationships between variables, you must write a testable hypothesis before compiling the data, performing analysis, and generating results to complete.

The data analysis and findings will help you test the hypothesis and see whether it is true or false. Here is all you need to know about how to write a hypothesis for a  dissertation .

Research Hypothesis Definition

Not sure what the meaning of the research hypothesis is?

A research hypothesis predicts an answer to the research question  based on existing theoretical knowledge or experimental data.

Some studies may have multiple hypothesis statements depending on the research question(s).  A research hypothesis must be based on formulas, facts, and theories. It should be testable by data analysis, observations, experiments, or other scientific methodologies that can refute or support the statement.

Variables in Hypothesis

Developing a hypothesis is easy. Most research studies have two or more variables in the hypothesis, particularly studies involving correlational and experimental research. The researcher can control or change the independent variable(s) while measuring and observing the independent variable(s).

“How long a student sleeps affects test scores.”

In the above statement, the dependent variable is the test score, while the independent variable is the length of time spent in sleep. Developing a hypothesis will be easy if you know your research’s dependent and independent variables.

Once you have developed a thesis statement, questions such as how to write a hypothesis for the dissertation and how to test a research hypothesis become pretty straightforward.

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Step-by-Step Guide on How to Write a Hypothesis

Here are the steps involved in how to write a hypothesis for a dissertation.

Step 1: Start with a Research Question

• Begin by asking a specific question about a topic of interest.
• This question should be clear, concise, and researchable.

Example: Does exposure to sunlight affect plant growth?

Step 2: Do Preliminary Research

• Before formulating a hypothesis, conduct background research to understand existing knowledge on the topic.
• Familiarise yourself with prior studies, theories, or observations related to the research question.

Step 3: Define Variables

• Independent Variable (IV): The factor that you change or manipulate in an experiment.
• Dependent Variable (DV): The factor that you measure.

Example: IV: Amount of sunlight exposure (e.g., 2 hours/day, 4 hours/day, 8 hours/day) DV: Plant growth (e.g., height in centimetres)

Step 4: Formulate the Hypothesis

• A hypothesis is a statement that predicts the relationship between variables.
• It is often written as an “if-then” statement.

Example: If plants receive more sunlight, then they will grow taller.

Step 5: Ensure it is Testable

A good hypothesis is empirically testable. This means you should be able to design an experiment or observation to test its validity.

Example: You can set up an experiment where plants are exposed to varying amounts of sunlight and then measure their growth over a period of time.

Step 6: Consider Potential Confounding Variables

• Confounding variables are factors other than the independent variable that might affect the outcome.
• It is important to identify these to ensure that they do not skew your results.

Example: Soil quality, water frequency, or type of plant can all affect growth. Consider keeping these constant in your experiment.

Step 7: Write the Null Hypothesis

• The null hypothesis is a statement that there is no effect or no relationship between the variables.
• It is what you aim to disprove or reject through your research.

Example: There is no difference in plant growth regardless of the amount of sunlight exposure.

Step 8: Test your Hypothesis

Design an experiment or conduct observations to test your hypothesis.

Example: Grow three sets of plants: one set exposed to 2 hours of sunlight daily, another exposed to 4 hours, and a third exposed to 8 hours. Measure and compare their growth after a set period.

Step 9: Analyse the Results

After testing, review your data to determine if it supports your hypothesis.

Step 10: Draw Conclusions

• Based on your findings, determine whether you can accept or reject the hypothesis.
• Remember, even if you reject your hypothesis, it’s a valuable result. It can guide future research and refine questions.

Three Ways to Phrase a Hypothesis

Try to use “if”… and “then”… to identify the variables. The independent variable should be present in the first part of the hypothesis, while the dependent variable will form the second part of the statement. Consider understanding the below research hypothesis example to create a specific, clear, and concise research hypothesis;

If an obese lady starts attending Zomba fitness classes, her health will improve.

In academic research, you can write the predicted variable relationship directly because most research studies correlate terms.

The number of Zomba fitness classes attended by the obese lady has a positive effect on health.

If your research compares two groups, then you can develop a hypothesis statement on their differences.

An obese lady who attended most Zumba fitness classes will have better health than those who attended a few.

How to Write a Null Hypothesis

If a statistical analysis is involved in your research, then you must create a null hypothesis. If you find any relationship between the variables, then the null hypothesis will be the default position that there is no relationship between them. H0 is the symbol for the null hypothesis, while the hypothesis is represented as H1. The null hypothesis will also answer your question, “How to test the research hypothesis in the dissertation.”

H0: The number of Zumba fitness classes attended by the obese lady does not affect her health.

H1: The number of Zumba fitness classes attended by obese lady positively affects health.

Also see:  Your Dissertation in Education

Hypothesis Examples

Research Question: Does the amount of sunlight a plant receives affect its growth? Hypothesis: Plants that receive more sunlight will grow taller than plants that receive less sunlight.

Research Question: Do students who eat breakfast perform better in school exams than those who don’t? Hypothesis: Students who eat a morning breakfast will score higher on school exams compared to students who skip breakfast.

Research Question: Does listening to music while studying impact a student’s ability to retain information? Hypothesis 1 (Directional): Students who listen to music while studying will retain less information than those who study in silence. Hypothesis 2 (Non-directional): There will be a difference in information retention between students who listen to music while studying and those who study in silence.

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If you are unsure about how to rest a research hypothesis in a dissertation or simply unsure about how to develop a hypothesis for your research, then you can take advantage of our dissertation services which cover every tiny aspect of a dissertation project you might need help with including but not limited to setting up a hypothesis and research questions,  help with individual chapters ,  full dissertation writing ,  statistical analysis , and much more.

Frequently Asked Questions

What are the 5 rules for writing a good hypothesis.

• Clear Statement: State a clear relationship between variables.
• Testable: Ensure it can be investigated and measured.
• Specific: Avoid vague terms, be precise in predictions.
• Falsifiable: Design to allow potential disproof.
• Relevant: Address research question and align with existing knowledge.

What is a hypothesis in simple words?

A hypothesis is an educated guess or prediction about something that can be tested. It is a statement that suggests a possible explanation for an event or phenomenon based on prior knowledge or observation. Scientists use hypotheses as a starting point for experiments to discover if they are true or false.

What is the hypothesis and examples?

A hypothesis is a testable prediction or explanation for an observation or phenomenon. For example, if plants are given sunlight, then they will grow. In this case, the hypothesis suggests that sunlight has a positive effect on plant growth. It can be tested by experimenting with plants in varying light conditions.

What is the hypothesis in research definition?

A hypothesis in research is a clear, testable statement predicting the possible outcome of a study based on prior knowledge and observation. It serves as the foundation for conducting experiments or investigations. Researchers test the validity of the hypothesis to draw conclusions and advance knowledge in a particular field.

Why is it called a hypothesis?

The term “hypothesis” originates from the Greek word “hypothesis,” which means “base” or “foundation.” It’s used to describe a foundational statement or proposition that can be tested. In scientific contexts, it denotes a tentative explanation for a phenomenon, serving as a starting point for investigation or experimentation.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Dissertation Structure & Layout 101: How to structure your dissertation, thesis or research project.

By: Derek Jansen (MBA) Reviewed By: David Phair (PhD) | July 2019

So, you’ve got a decent understanding of what a dissertation is , you’ve chosen your topic and hopefully you’ve received approval for your research proposal . Awesome! Now its time to start the actual dissertation or thesis writing journey.

To craft a high-quality document, the very first thing you need to understand is dissertation structure . In this post, we’ll walk you through the generic dissertation structure and layout, step by step. We’ll start with the big picture, and then zoom into each chapter to briefly discuss the core contents. If you’re just starting out on your research journey, you should start with this post, which covers the big-picture process of how to write a dissertation or thesis .

*The Caveat *

In this post, we’ll be discussing a traditional dissertation/thesis structure and layout, which is generally used for social science research across universities, whether in the US, UK, Europe or Australia. However, some universities may have small variations on this structure (extra chapters, merged chapters, slightly different ordering, etc).

So, always check with your university if they have a prescribed structure or layout that they expect you to work with. If not, it’s safe to assume the structure we’ll discuss here is suitable. And even if they do have a prescribed structure, you’ll still get value from this post as we’ll explain the core contents of each section.  

Overview: S tructuring a dissertation or thesis

• Acknowledgements page
• Abstract (or executive summary)
• Table of contents , list of figures and tables
• Chapter 1: Introduction
• Chapter 2: Literature review
• Chapter 3: Methodology
• Chapter 4: Results
• Chapter 5: Discussion
• Chapter 6: Conclusion
• Reference list

As I mentioned, some universities will have slight variations on this structure. For example, they want an additional “personal reflection chapter”, or they might prefer the results and discussion chapter to be merged into one. Regardless, the overarching flow will always be the same, as this flow reflects the research process , which we discussed here – i.e.:

• The introduction chapter presents the core research question and aims .
• The literature review chapter assesses what the current research says about this question.
• The methodology, results and discussion chapters go about undertaking new research about this question.
• The conclusion chapter (attempts to) answer the core research question .

In other words, the dissertation structure and layout reflect the research process of asking a well-defined question(s), investigating, and then answering the question – see below.

To restate that – the structure and layout of a dissertation reflect the flow of the overall research process . This is essential to understand, as each chapter will make a lot more sense if you “get” this concept. If you’re not familiar with the research process, read this post before going further.

Right. Now that we’ve covered the big picture, let’s dive a little deeper into the details of each section and chapter. Oh and by the way, you can also grab our free dissertation/thesis template here to help speed things up.

The title page of your dissertation is the very first impression the marker will get of your work, so it pays to invest some time thinking about your title. But what makes for a good title? A strong title needs to be 3 things:

• Succinct (not overly lengthy or verbose)
• Specific (not vague or ambiguous)
• Representative of the research you’re undertaking (clearly linked to your research questions)

Typically, a good title includes mention of the following:

• The broader area of the research (i.e. the overarching topic)
• The specific focus of your research (i.e. your specific context)
• Indication of research design (e.g. quantitative , qualitative , or  mixed methods ).

For example:

A quantitative investigation [research design] into the antecedents of organisational trust [broader area] in the UK retail forex trading market [specific context/area of focus].

Again, some universities may have specific requirements regarding the format and structure of the title, so it’s worth double-checking expectations with your institution (if there’s no mention in the brief or study material).

Acknowledgements

This page provides you with an opportunity to say thank you to those who helped you along your research journey. Generally, it’s optional (and won’t count towards your marks), but it is academic best practice to include this.

So, who do you say thanks to? Well, there’s no prescribed requirements, but it’s common to mention the following people:

• Your dissertation supervisor or committee.
• Any professors, lecturers or academics that helped you understand the topic or methodologies.
• Any tutors, mentors or advisors.
• Your family and friends, especially spouse (for adult learners studying part-time).

There’s no need for lengthy rambling. Just state who you’re thankful to and for what (e.g. thank you to my supervisor, John Doe, for his endless patience and attentiveness) – be sincere. In terms of length, you should keep this to a page or less.

Abstract or executive summary

The dissertation abstract (or executive summary for some degrees) serves to provide the first-time reader (and marker or moderator) with a big-picture view of your research project. It should give them an understanding of the key insights and findings from the research, without them needing to read the rest of the report – in other words, it should be able to stand alone .

For it to stand alone, your abstract should cover the following key points (at a minimum):

• Your research questions and aims – what key question(s) did your research aim to answer?
• Your methodology – how did you go about investigating the topic and finding answers to your research question(s)?
• Your findings – following your own research, what did do you discover?
• Your conclusions – based on your findings, what conclusions did you draw? What answers did you find to your research question(s)?

So, in much the same way the dissertation structure mimics the research process, your abstract or executive summary should reflect the research process, from the initial stage of asking the original question to the final stage of answering that question.

In practical terms, it’s a good idea to write this section up last , once all your core chapters are complete. Otherwise, you’ll end up writing and rewriting this section multiple times (just wasting time). For a step by step guide on how to write a strong executive summary, check out this post .

Need a helping hand?

Table of contents

This section is straightforward. You’ll typically present your table of contents (TOC) first, followed by the two lists – figures and tables. I recommend that you use Microsoft Word’s automatic table of contents generator to generate your TOC. If you’re not familiar with this functionality, the video below explains it simply:

If you find that your table of contents is overly lengthy, consider removing one level of depth. Oftentimes, this can be done without detracting from the usefulness of the TOC.

Right, now that the “admin” sections are out of the way, its time to move on to your core chapters. These chapters are the heart of your dissertation and are where you’ll earn the marks. The first chapter is the introduction chapter – as you would expect, this is the time to introduce your research…

It’s important to understand that even though you’ve provided an overview of your research in your abstract, your introduction needs to be written as if the reader has not read that (remember, the abstract is essentially a standalone document). So, your introduction chapter needs to start from the very beginning, and should address the following questions:

• What will you be investigating (in plain-language, big picture-level)?
• Why is that worth investigating? How is it important to academia or business? How is it sufficiently original?
• What are your research aims and research question(s)? Note that the research questions can sometimes be presented at the end of the literature review (next chapter).
• What is the scope of your study? In other words, what will and won’t you cover ?
• How will you approach your research? In other words, what methodology will you adopt?
• How will you structure your dissertation? What are the core chapters and what will you do in each of them?

These are just the bare basic requirements for your intro chapter. Some universities will want additional bells and whistles in the intro chapter, so be sure to carefully read your brief or consult your research supervisor.

If done right, your introduction chapter will set a clear direction for the rest of your dissertation. Specifically, it will make it clear to the reader (and marker) exactly what you’ll be investigating, why that’s important, and how you’ll be going about the investigation. Conversely, if your introduction chapter leaves a first-time reader wondering what exactly you’ll be researching, you’ve still got some work to do.

Now that you’ve set a clear direction with your introduction chapter, the next step is the literature review . In this section, you will analyse the existing research (typically academic journal articles and high-quality industry publications), with a view to understanding the following questions:

• What does the literature currently say about the topic you’re investigating?
• Is the literature lacking or well established? Is it divided or in disagreement?
• How does your research fit into the bigger picture?
• How does your research contribute something original?
• How does the methodology of previous studies help you develop your own?

Depending on the nature of your study, you may also present a conceptual framework towards the end of your literature review, which you will then test in your actual research.

Again, some universities will want you to focus on some of these areas more than others, some will have additional or fewer requirements, and so on. Therefore, as always, its important to review your brief and/or discuss with your supervisor, so that you know exactly what’s expected of your literature review chapter.

Now that you’ve investigated the current state of knowledge in your literature review chapter and are familiar with the existing key theories, models and frameworks, its time to design your own research. Enter the methodology chapter – the most “science-ey” of the chapters…

In this chapter, you need to address two critical questions:

• Exactly HOW will you carry out your research (i.e. what is your intended research design)?
• Exactly WHY have you chosen to do things this way (i.e. how do you justify your design)?

Remember, the dissertation part of your degree is first and foremost about developing and demonstrating research skills . Therefore, the markers want to see that you know which methods to use, can clearly articulate why you’ve chosen then, and know how to deploy them effectively.

Importantly, this chapter requires detail – don’t hold back on the specifics. State exactly what you’ll be doing, with who, when, for how long, etc. Moreover, for every design choice you make, make sure you justify it.

In practice, you will likely end up coming back to this chapter once you’ve undertaken all your data collection and analysis, and revise it based on changes you made during the analysis phase. This is perfectly fine. Its natural for you to add an additional analysis technique, scrap an old one, etc based on where your data lead you. Of course, I’m talking about small changes here – not a fundamental switch from qualitative to quantitative, which will likely send your supervisor in a spin!

You’ve now collected your data and undertaken your analysis, whether qualitative, quantitative or mixed methods. In this chapter, you’ll present the raw results of your analysis . For example, in the case of a quant study, you’ll present the demographic data, descriptive statistics, inferential statistics , etc.

Typically, Chapter 4 is simply a presentation and description of the data, not a discussion of the meaning of the data. In other words, it’s descriptive, rather than analytical – the meaning is discussed in Chapter 5. However, some universities will want you to combine chapters 4 and 5, so that you both present and interpret the meaning of the data at the same time. Check with your institution what their preference is.

Now that you’ve presented the data analysis results, its time to interpret and analyse them. In other words, its time to discuss what they mean, especially in relation to your research question(s).

What you discuss here will depend largely on your chosen methodology. For example, if you’ve gone the quantitative route, you might discuss the relationships between variables . If you’ve gone the qualitative route, you might discuss key themes and the meanings thereof. It all depends on what your research design choices were.

Most importantly, you need to discuss your results in relation to your research questions and aims, as well as the existing literature. What do the results tell you about your research questions? Are they aligned with the existing research or at odds? If so, why might this be? Dig deep into your findings and explain what the findings suggest, in plain English.

The final chapter – you’ve made it! Now that you’ve discussed your interpretation of the results, its time to bring it back to the beginning with the conclusion chapter . In other words, its time to (attempt to) answer your original research question s (from way back in chapter 1). Clearly state what your conclusions are in terms of your research questions. This might feel a bit repetitive, as you would have touched on this in the previous chapter, but its important to bring the discussion full circle and explicitly state your answer(s) to the research question(s).

Next, you’ll typically discuss the implications of your findings . In other words, you’ve answered your research questions – but what does this mean for the real world (or even for academia)? What should now be done differently, given the new insight you’ve generated?

Lastly, you should discuss the limitations of your research, as well as what this means for future research in the area. No study is perfect, especially not a Masters-level. Discuss the shortcomings of your research. Perhaps your methodology was limited, perhaps your sample size was small or not representative, etc, etc. Don’t be afraid to critique your work – the markers want to see that you can identify the limitations of your work. This is a strength, not a weakness. Be brutal!

This marks the end of your core chapters – woohoo! From here on out, it’s pretty smooth sailing.

The reference list is straightforward. It should contain a list of all resources cited in your dissertation, in the required format, e.g. APA , Harvard, etc.

It’s essential that you use reference management software for your dissertation. Do NOT try handle your referencing manually – its far too error prone. On a reference list of multiple pages, you’re going to make mistake. To this end, I suggest considering either Mendeley or Zotero. Both are free and provide a very straightforward interface to ensure that your referencing is 100% on point. I’ve included a simple how-to video for the Mendeley software (my personal favourite) below:

Some universities may ask you to include a bibliography, as opposed to a reference list. These two things are not the same . A bibliography is similar to a reference list, except that it also includes resources which informed your thinking but were not directly cited in your dissertation. So, double-check your brief and make sure you use the right one.

The very last piece of the puzzle is the appendix or set of appendices. This is where you’ll include any supporting data and evidence. Importantly, supporting is the keyword here.

Your appendices should provide additional “nice to know”, depth-adding information, which is not critical to the core analysis. Appendices should not be used as a way to cut down word count (see this post which covers how to reduce word count ). In other words, don’t place content that is critical to the core analysis here, just to save word count. You will not earn marks on any content in the appendices, so don’t try to play the system!

Time to recap…

And there you have it – the traditional dissertation structure and layout, from A-Z. To recap, the core structure for a dissertation or thesis is (typically) as follows:

• Acknowledgments page

Most importantly, the core chapters should reflect the research process (asking, investigating and answering your research question). Moreover, the research question(s) should form the golden thread throughout your dissertation structure. Everything should revolve around the research questions, and as you’ve seen, they should form both the start point (i.e. introduction chapter) and the endpoint (i.e. conclusion chapter).

I hope this post has provided you with clarity about the traditional dissertation/thesis structure and layout. If you have any questions or comments, please leave a comment below, or feel free to get in touch with us. Also, be sure to check out the rest of the  Grad Coach Blog .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

36 Comments

many thanks i found it very useful

Glad to hear that, Arun. Good luck writing your dissertation.

Such clear practical logical advice. I very much needed to read this to keep me focused in stead of fretting.. Perfect now ready to start my research!

what about scientific fields like computer or engineering thesis what is the difference in the structure? thank you very much

Thanks so much this helped me a lot!

Very helpful and accessible. What I like most is how practical the advice is along with helpful tools/ links.

Thanks Ade!

Thank you so much sir.. It was really helpful..

You’re welcome!

Hi! How many words maximum should contain the abstract?

Thank you so much 😊 Find this at the right moment

You’re most welcome. Good luck with your dissertation.

best ever benefit i got on right time thank you

Many times Clarity and vision of destination of dissertation is what makes the difference between good ,average and great researchers the same way a great automobile driver is fast with clarity of address and Clear weather conditions .

I guess Great researcher = great ideas + knowledge + great and fast data collection and modeling + great writing + high clarity on all these

You have given immense clarity from start to end.

Morning. Where will I write the definitions of what I’m referring to in my report?

Thank you so much Derek, I was almost lost! Thanks a tonnnn! Have a great day!

Thanks ! so concise and valuable

This was very helpful. Clear and concise. I know exactly what to do now.

Thank you for allowing me to go through briefly. I hope to find time to continue.

Really useful to me. Thanks a thousand times

Very interesting! It will definitely set me and many more for success. highly recommended.

Thank you soo much sir, for the opportunity to express my skills

Usefull, thanks a lot. Really clear

Very nice and easy to understand. Thank you .

That was incredibly useful. Thanks Grad Coach Crew!

My stress level just dropped at least 15 points after watching this. Just starting my thesis for my grad program and I feel a lot more capable now! Thanks for such a clear and helpful video, Emma and the GradCoach team!

Do we need to mention the number of words the dissertation contains in the main document?

It depends on your university’s requirements, so it would be best to check with them 🙂

Such a helpful post to help me get started with structuring my masters dissertation, thank you!

Great video; I appreciate that helpful information

It is so necessary or avital course

This blog is very informative for my research. Thank you

Doctoral students are required to fill out the National Research Council’s Survey of Earned Doctorates

wow this is an amazing gain in my life

This is so good

How can i arrange my specific objectives in my dissertation?

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Guide to writing your thesis/dissertation, definition of dissertation and thesis.

The dissertation or thesis is a scholarly treatise that substantiates a specific point of view as a result of original research that is conducted by students during their graduate study. At Cornell, the thesis is a requirement for the receipt of the M.A. and M.S. degrees and some professional master’s degrees. The dissertation is a requirement of the Ph.D. degree.

Formatting Requirement and Standards

The Graduate School sets the minimum format for your thesis or dissertation, while you, your special committee, and your advisor/chair decide upon the content and length. Grammar, punctuation, spelling, and other mechanical issues are your sole responsibility. Generally, the thesis and dissertation should conform to the standards of leading academic journals in your field. The Graduate School does not monitor the thesis or dissertation for mechanics, content, or style.

“Papers Option” Dissertation or Thesis

A “papers option” is available only to students in certain fields, which are listed on the Fields Permitting the Use of Papers Option page , or by approved petition. If you choose the papers option, your dissertation or thesis is organized as a series of relatively independent chapters or papers that you have submitted or will be submitting to journals in the field. You must be the only author or the first author of the papers to be used in the dissertation. The papers-option dissertation or thesis must meet all format and submission requirements, and a singular referencing convention must be used throughout.

ProQuest Electronic Submissions

The dissertation and thesis become permanent records of your original research, and in the case of doctoral research, the Graduate School requires publication of the dissertation and abstract in its original form. All Cornell master’s theses and doctoral dissertations require an electronic submission through ProQuest, which fills orders for paper or digital copies of the thesis and dissertation and makes a digital version available online via their subscription database, ProQuest Dissertations & Theses . For master’s theses, only the abstract is available. ProQuest provides worldwide distribution of your work from the master copy. You retain control over your dissertation and are free to grant publishing rights as you see fit. The formatting requirements contained in this guide meet all ProQuest specifications.

Copies of Dissertation and Thesis

Copies of Ph.D. dissertations and master’s theses are also uploaded in PDF format to the Cornell Library Repository, eCommons . A print copy of each master’s thesis and doctoral dissertation is submitted to Cornell University Library by ProQuest.

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How to Write the Rationale of the Study in Research (Examples)

What is the Rationale of the Study?

The rationale of the study is the justification for taking on a given study. It explains the reason the study was conducted or should be conducted. This means the study rationale should explain to the reader or examiner why the study is/was necessary. It is also sometimes called the “purpose” or “justification” of a study. While this is not difficult to grasp in itself, you might wonder how the rationale of the study is different from your research question or from the statement of the problem of your study, and how it fits into the rest of your thesis or research paper. 

The rationale of the study links the background of the study to your specific research question and justifies the need for the latter on the basis of the former. In brief, you first provide and discuss existing data on the topic, and then you tell the reader, based on the background evidence you just presented, where you identified gaps or issues and why you think it is important to address those. The problem statement, lastly, is the formulation of the specific research question you choose to investigate, following logically from your rationale, and the approach you are planning to use to do that.

Table of Contents:

How to write a rationale for a research paper , how do you justify the need for a research study.

• Study Rationale Example: Where Does It Go In Your Paper?

The basis for writing a research rationale is preliminary data or a clear description of an observation. If you are doing basic/theoretical research, then a literature review will help you identify gaps in current knowledge. In applied/practical research, you base your rationale on an existing issue with a certain process (e.g., vaccine proof registration) or practice (e.g., patient treatment) that is well documented and needs to be addressed. By presenting the reader with earlier evidence or observations, you can (and have to) convince them that you are not just repeating what other people have already done or said and that your ideas are not coming out of thin air. 

Once you have explained where you are coming from, you should justify the need for doing additional research–this is essentially the rationale of your study. Finally, when you have convinced the reader of the purpose of your work, you can end your introduction section with the statement of the problem of your research that contains clear aims and objectives and also briefly describes (and justifies) your methodological approach. 

When is the Rationale for Research Written?

The author can present the study rationale both before and after the research is conducted. 

• Before conducting research : The study rationale is a central component of the research proposal . It represents the plan of your work, constructed before the study is actually executed.
• Once research has been conducted : After the study is completed, the rationale is presented in a research article or  PhD dissertation  to explain why you focused on this specific research question. When writing the study rationale for this purpose, the author should link the rationale of the research to the aims and outcomes of the study.

What to Include in the Study Rationale

Although every study rationale is different and discusses different specific elements of a study’s method or approach, there are some elements that should be included to write a good rationale. Make sure to touch on the following:

• A summary of conclusions from your review of the relevant literature
• What is currently unknown (gaps in knowledge)
• Inconclusive or contested results  from previous studies on the same or similar topic
• The necessity to improve or build on previous research, such as to improve methodology or utilize newer techniques and/or technologies

There are different types of limitations that you can use to justify the need for your study. In applied/practical research, the justification for investigating something is always that an existing process/practice has a problem or is not satisfactory. Let’s say, for example, that people in a certain country/city/community commonly complain about hospital care on weekends (not enough staff, not enough attention, no decisions being made), but you looked into it and realized that nobody ever investigated whether these perceived problems are actually based on objective shortages/non-availabilities of care or whether the lower numbers of patients who are treated during weekends are commensurate with the provided services.

In this case, “lack of data” is your justification for digging deeper into the problem. Or, if it is obvious that there is a shortage of staff and provided services on weekends, you could decide to investigate which of the usual procedures are skipped during weekends as a result and what the negative consequences are. 

In basic/theoretical research, lack of knowledge is of course a common and accepted justification for additional research—but make sure that it is not your only motivation. “Nobody has ever done this” is only a convincing reason for a study if you explain to the reader why you think we should know more about this specific phenomenon. If there is earlier research but you think it has limitations, then those can usually be classified into “methodological”, “contextual”, and “conceptual” limitations. To identify such limitations, you can ask specific questions and let those questions guide you when you explain to the reader why your study was necessary:

Methodological limitations

• Did earlier studies try but failed to measure/identify a specific phenomenon?
• Was earlier research based on incorrect conceptualizations of variables?
• Were earlier studies based on questionable operationalizations of key concepts?
• Did earlier studies use questionable or inappropriate research designs?

Contextual limitations

• Have recent changes in the studied problem made previous studies irrelevant?
• Are you studying a new/particular context that previous findings do not apply to?

Conceptual limitations

• Do previous findings only make sense within a specific framework or ideology?

Study Rationale Examples

Let’s look at an example from one of our earlier articles on the statement of the problem to clarify how your rationale fits into your introduction section. This is a very short introduction for a practical research study on the challenges of online learning. Your introduction might be much longer (especially the context/background section), and this example does not contain any sources (which you will have to provide for all claims you make and all earlier studies you cite)—but please pay attention to how the background presentation , rationale, and problem statement blend into each other in a logical way so that the reader can follow and has no reason to question your motivation or the foundation of your research.

Background presentation

Since the beginning of the Covid pandemic, most educational institutions around the world have transitioned to a fully online study model, at least during peak times of infections and social distancing measures. This transition has not been easy and even two years into the pandemic, problems with online teaching and studying persist (reference needed) . 

While the increasing gap between those with access to technology and equipment and those without access has been determined to be one of the main challenges (reference needed) , others claim that online learning offers more opportunities for many students by breaking down barriers of location and distance (reference needed) .  

Rationale of the study

Since teachers and students cannot wait for circumstances to go back to normal, the measures that schools and universities have implemented during the last two years, their advantages and disadvantages, and the impact of those measures on students’ progress, satisfaction, and well-being need to be understood so that improvements can be made and demographics that have been left behind can receive the support they need as soon as possible.

Statement of the problem

To identify what changes in the learning environment were considered the most challenging and how those changes relate to a variety of student outcome measures, we conducted surveys and interviews among teachers and students at ten institutions of higher education in four different major cities, two in the US (New York and Chicago), one in South Korea (Seoul), and one in the UK (London). Responses were analyzed with a focus on different student demographics and how they might have been affected differently by the current situation.

How long is a study rationale?

In a research article bound for journal publication, your rationale should not be longer than a few sentences (no longer than one brief paragraph). A  dissertation or thesis  usually allows for a longer description; depending on the length and nature of your document, this could be up to a couple of paragraphs in length. A completely novel or unconventional approach might warrant a longer and more detailed justification than an approach that slightly deviates from well-established methods and approaches.

Consider Using Professional Academic Editing Services

Now that you know how to write the rationale of the study for a research proposal or paper, you should make use of Wordvice AI’s free AI Grammar Checker , or receive professional academic proofreading services from Wordvice, including research paper editing services and manuscript editing services to polish your submitted research documents.

You can also find many more articles, for example on writing the other parts of your research paper , on choosing a title , or on making sure you understand and adhere to the author instructions before you submit to a journal, on the Wordvice academic resources pages.

What Is the Hypothesis in a Dissertation?

Types of Research Hypotheses

Your dissertation hypothesis is the prediction statement based on the theory that you are researching in your study. Doctoral candidates test their hypotheses in their dissertations, their original research project that they write and defend in order to graduate. Here, you will learn about hypothesis types, writing and testing for your dissertation and hypothesis examples.

In your dissertation, you may create a hypothesis based on your research that predicts a relationship, called an "alternative" or "research" hypothesis. To balance your findings, you will also create a "null" hypothesis, which claims that the relationship that is to be proven in the research hypothesis does not exist. According to Alan Agresti and Barbara Finlay in “Statistical Methods for the Social Sciences,” the null is directly tested and predicts no effect, and the alternative contradicts the null and predicts an effect.

There can also be types of research hypotheses. As indicated in Research Methods Knowledge Base, a "one-tailed" hypothesis specifies a direction, either an increase or a decrease, while a "two-tailed" hypothesis does not specify a direction, only a change.

You must write your dissertation hypotheses before you collect and analyze your data. A useful hypothesis as testable and should include the independent variable, which you control, and the dependent variable, which is observed or measured based on the independent variable. For example, taking media consumption of violence as the independent variable and aggression as the dependent variable, the null hypothesis could state, “Media consumption of violence has no effect on aggression,” while the alternative hypothesis would state, “Media consumption of violence has an effect on aggression.” Similarly, if you wanted to create a one-tailed hypothesis, you would indicate a direction, such as, “Media consumption of violence increases aggression." Make sure that your statements are brief and straight-to-the-point and keep in mind the results you will measure in your study.

Research Methods Knowledge Base states that hypothesis testing assumes that both mutually exclusive hypotheses (research and null) exhaust every possible outcome and in the end, one is accepted and the other is rejected. When you analyze your data, you conclude whether you reject your null hypothesis and accept your alternative or fail to reject your null.

A dissertation can test a very broad range of hypotheses, depending on the discipline and focus of the writer. For example Antoinette Hill from Our Lady of the Lake University lent her research hypothesis to the title of her dissertation, "Are There Differences in Leadership Styles at Local, State, and National/Federal Levels among Advocates for People with Disabilities?" Her hypothesis used leadership as the independent variable and level of advocacy for people with disabilities as the dependent variable. Simply looking through the titles of dissertations published at a university of college each year can provide a long list of examples of a variety of hypotheses and ways to present them.

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• Our Lady of the Lake University; Are There Differences in Leadership Styles at Local, State, and National/Federal Levels among Advocates for People with Disabilities?; Antoinette J.G. Hill
• Our Lady of the Lake University; Do We March to the Beat of a Different Drum? Examining the Differences in the Perception of Leadership Styles between Academic Teachers and Music Teachers; Emma Yvette Dromgoole
• Research Methods Knowledge Base: Hypotheses; William M.K. Trochim
• Statistical Methods for the Social Sciences; Alan Agresti and Barbara Finlay
• Online Stat Book: Type I and II Errors; David M. Lane et al.

Elaine J. Dispo, a journalist since 1996, specializes in education. She wrote for “Fil-Am Press.” Dispo earned the Texas Intercollegiate Press Association Frank W. Buckley Scholarship and the Students In Free Enterprise Sam M. Walton Fellowship. She holds her B.A. and M.A. in Communication and is a Ph.D. candidate.

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What is a thesis | A Complete Guide with Examples

Table of Contents

A thesis is a comprehensive academic paper based on your original research that presents new findings, arguments, and ideas of your study. It’s typically submitted at the end of your master’s degree or as a capstone of your bachelor’s degree.

However, writing a thesis can be laborious, especially for beginners. From the initial challenge of pinpointing a compelling research topic to organizing and presenting findings, the process is filled with potential pitfalls.

Therefore, to help you, this guide talks about what is a thesis. Additionally, it offers revelations and methodologies to transform it from an overwhelming task to a manageable and rewarding academic milestone.

What is a thesis?

A thesis is an in-depth research study that identifies a particular topic of inquiry and presents a clear argument or perspective about that topic using evidence and logic.

Writing a thesis showcases your ability of critical thinking, gathering evidence, and making a compelling argument. Integral to these competencies is thorough research, which not only fortifies your propositions but also confers credibility to your entire study.

Furthermore, there's another phenomenon you might often confuse with the thesis: the ' working thesis .' However, they aren't similar and shouldn't be used interchangeably.

A working thesis, often referred to as a preliminary or tentative thesis, is an initial version of your thesis statement. It serves as a draft or a starting point that guides your research in its early stages.

As you research more and gather more evidence, your initial thesis (aka working thesis) might change. It's like a starting point that can be adjusted as you learn more. It's normal for your main topic to change a few times before you finalize it.

While a thesis identifies and provides an overarching argument, the key to clearly communicating the central point of that argument lies in writing a strong thesis statement.

What is a thesis statement?

A strong thesis statement (aka thesis sentence) is a concise summary of the main argument or claim of the paper. It serves as a critical anchor in any academic work, succinctly encapsulating the primary argument or main idea of the entire paper.

Typically found within the introductory section, a strong thesis statement acts as a roadmap of your thesis, directing readers through your arguments and findings. By delineating the core focus of your investigation, it offers readers an immediate understanding of the context and the gravity of your study.

Furthermore, an effectively crafted thesis statement can set forth the boundaries of your research, helping readers anticipate the specific areas of inquiry you are addressing.

Different types of thesis statements

A good thesis statement is clear, specific, and arguable. Therefore, it is necessary for you to choose the right type of thesis statement for your academic papers.

Thesis statements can be classified based on their purpose and structure. Here are the primary types of thesis statements:

Argumentative (or Persuasive) thesis statement

Purpose : To convince the reader of a particular stance or point of view by presenting evidence and formulating a compelling argument.

Example : Reducing plastic use in daily life is essential for environmental health.

Analytical thesis statement

Purpose : To break down an idea or issue into its components and evaluate it.

Example : By examining the long-term effects, social implications, and economic impact of climate change, it becomes evident that immediate global action is necessary.

Expository (or Descriptive) thesis statement

Purpose : To explain a topic or subject to the reader.

Example : The Great Depression, spanning the 1930s, was a severe worldwide economic downturn triggered by a stock market crash, bank failures, and reduced consumer spending.

Cause and effect thesis statement

Purpose : To demonstrate a cause and its resulting effect.

Example : Overuse of smartphones can lead to impaired sleep patterns, reduced face-to-face social interactions, and increased levels of anxiety.

Compare and contrast thesis statement

Purpose : To highlight similarities and differences between two subjects.

Example : "While both novels '1984' and 'Brave New World' delve into dystopian futures, they differ in their portrayal of individual freedom, societal control, and the role of technology."

When you write a thesis statement , it's important to ensure clarity and precision, so the reader immediately understands the central focus of your work.

What is the difference between a thesis and a thesis statement?

While both terms are frequently used interchangeably, they have distinct meanings.

A thesis refers to the entire research document, encompassing all its chapters and sections. In contrast, a thesis statement is a brief assertion that encapsulates the central argument of the research.

Here’s an in-depth differentiation table of a thesis and a thesis statement.

Now, to craft a compelling thesis, it's crucial to adhere to a specific structure. Let’s break down these essential components that make up a thesis structure

15 components of a thesis structure

Navigating a thesis can be daunting. However, understanding its structure can make the process more manageable.

Here are the key components or different sections of a thesis structure:

Your thesis begins with the title page. It's not just a formality but the gateway to your research.

Here, you'll prominently display the necessary information about you (the author) and your institutional details.

• Title of your thesis
• Your full name
• Your department
• Your institution and degree program
• Your submission date
• Your Supervisor's name (in some cases)
• Your Department or faculty (in some cases)
• Your University's logo (in some cases)
• Your Student ID (in some cases)

In a concise manner, you'll have to summarize the critical aspects of your research in typically no more than 200-300 words.

This includes the problem statement, methodology, key findings, and conclusions. For many, the abstract will determine if they delve deeper into your work, so ensure it's clear and compelling.

Acknowledgments

Research is rarely a solitary endeavor. In the acknowledgments section, you have the chance to express gratitude to those who've supported your journey.

This might include advisors, peers, institutions, or even personal sources of inspiration and support. It's a personal touch, reflecting the humanity behind the academic rigor.

Table of contents

A roadmap for your readers, the table of contents lists the chapters, sections, and subsections of your thesis.

By providing page numbers, you allow readers to navigate your work easily, jumping to sections that pique their interest.

List of figures and tables

Research often involves data, and presenting this data visually can enhance understanding. This section provides an organized listing of all figures and tables in your thesis.

It's a visual index, ensuring that readers can quickly locate and reference your graphical data.

Introduction

Here's where you introduce your research topic, articulate the research question or objective, and outline the significance of your study.

• Present the research topic : Clearly articulate the central theme or subject of your research.
• Background information : Ground your research topic, providing any necessary context or background information your readers might need to understand the significance of your study.
• Define the scope : Clearly delineate the boundaries of your research, indicating what will and won't be covered.
• Literature review : Introduce any relevant existing research on your topic, situating your work within the broader academic conversation and highlighting where your research fits in.
• State the research Question(s) or objective(s) : Clearly articulate the primary questions or objectives your research aims to address.
• Outline the study's structure : Give a brief overview of how the subsequent sections of your work will unfold, guiding your readers through the journey ahead.

The introduction should captivate your readers, making them eager to delve deeper into your research journey.

Literature review section

Your study correlates with existing research. Therefore, in the literature review section, you'll engage in a dialogue with existing knowledge, highlighting relevant studies, theories, and findings.

It's here that you identify gaps in the current knowledge, positioning your research as a bridge to new insights.

To streamline this process, consider leveraging AI tools. For example, the SciSpace literature review tool enables you to efficiently explore and delve into research papers, simplifying your literature review journey.

Methodology

In the research methodology section, you’ll detail the tools, techniques, and processes you employed to gather and analyze data. This section will inform the readers about how you approached your research questions and ensures the reproducibility of your study.

Here's a breakdown of what it should encompass:

• Research Design : Describe the overall structure and approach of your research. Are you conducting a qualitative study with in-depth interviews? Or is it a quantitative study using statistical analysis? Perhaps it's a mixed-methods approach?
• Data Collection : Detail the methods you used to gather data. This could include surveys, experiments, observations, interviews, archival research, etc. Mention where you sourced your data, the duration of data collection, and any tools or instruments used.
• Sampling : If applicable, explain how you selected participants or data sources for your study. Discuss the size of your sample and the rationale behind choosing it.
• Data Analysis : Describe the techniques and tools you used to process and analyze the data. This could range from statistical tests in quantitative research to thematic analysis in qualitative research.
• Validity and Reliability : Address the steps you took to ensure the validity and reliability of your findings to ensure that your results are both accurate and consistent.
• Ethical Considerations : Highlight any ethical issues related to your research and the measures you took to address them, including — informed consent, confidentiality, and data storage and protection measures.

Moreover, different research questions necessitate different types of methodologies. For instance:

• Experimental methodology : Often used in sciences, this involves a controlled experiment to discern causality.
• Qualitative methodology : Employed when exploring patterns or phenomena without numerical data. Methods can include interviews, focus groups, or content analysis.
• Quantitative methodology : Concerned with measurable data and often involves statistical analysis. Surveys and structured observations are common tools here.
• Mixed methods : As the name implies, this combines both qualitative and quantitative methodologies.

The Methodology section isn’t just about detailing the methods but also justifying why they were chosen. The appropriateness of the methods in addressing your research question can significantly impact the credibility of your findings.

Results (or Findings)

This section presents the outcomes of your research. It's crucial to note that the nature of your results may vary; they could be quantitative, qualitative, or a mix of both.

Quantitative results often present statistical data, showcasing measurable outcomes, and they benefit from tables, graphs, and figures to depict these data points.

Qualitative results , on the other hand, might delve into patterns, themes, or narratives derived from non-numerical data, such as interviews or observations.

Regardless of the nature of your results, clarity is essential. This section is purely about presenting the data without offering interpretations — that comes later in the discussion.

In the discussion section, the raw data transforms into valuable insights.

Start by revisiting your research question and contrast it with the findings. How do your results expand, constrict, or challenge current academic conversations?

Dive into the intricacies of the data, guiding the reader through its implications. Detail potential limitations transparently, signaling your awareness of the research's boundaries. This is where your academic voice should be resonant and confident.

Practical implications (Recommendation) section

Based on the insights derived from your research, this section provides actionable suggestions or proposed solutions.

Whether aimed at industry professionals or the general public, recommendations translate your academic findings into potential real-world actions. They help readers understand the practical implications of your work and how it can be applied to effect change or improvement in a given field.

When crafting recommendations, it's essential to ensure they're feasible and rooted in the evidence provided by your research. They shouldn't merely be aspirational but should offer a clear path forward, grounded in your findings.

The conclusion provides closure to your research narrative.

It's not merely a recap but a synthesis of your main findings and their broader implications. Reconnect with the research questions or hypotheses posited at the beginning, offering clear answers based on your findings.

Reflect on the broader contributions of your study, considering its impact on the academic community and potential real-world applications.

Lastly, the conclusion should leave your readers with a clear understanding of the value and impact of your study.

References (or Bibliography)

Every theory you've expounded upon, every data point you've cited, and every methodological precedent you've followed finds its acknowledgment here.

In references, it's crucial to ensure meticulous consistency in formatting, mirroring the specific guidelines of the chosen citation style .

Proper referencing helps to avoid plagiarism , gives credit to original ideas, and allows readers to explore topics of interest. Moreover, it situates your work within the continuum of academic knowledge.

To properly cite the sources used in the study, you can rely on online citation generator tools  to generate accurate citations!

Here’s more on how you can cite your sources.

Often, the depth of research produces a wealth of material that, while crucial, can make the core content of the thesis cumbersome. The appendix is where you mention extra information that supports your research but isn't central to the main text.

Whether it's raw datasets, detailed procedural methodologies, extended case studies, or any other ancillary material, the appendices ensure that these elements are archived for reference without breaking the main narrative's flow.

For thorough researchers and readers keen on meticulous details, the appendices provide a treasure trove of insights.

Glossary (optional)

In academics, specialized terminologies, and jargon are inevitable. However, not every reader is versed in every term.

The glossary, while optional, is a critical tool for accessibility. It's a bridge ensuring that even readers from outside the discipline can access, understand, and appreciate your work.

By defining complex terms and providing context, you're inviting a wider audience to engage with your research, enhancing its reach and impact.

Remember, while these components provide a structured framework, the essence of your thesis lies in the originality of your ideas, the rigor of your research, and the clarity of your presentation.

As you craft each section, keep your readers in mind, ensuring that your passion and dedication shine through every page.

Thesis examples

To further elucidate the concept of a thesis, here are illustrative examples from various fields:

Example 1 (History): Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807 by Suchait Kahlon.

Example 2 (Climate Dynamics): Influence of external forcings on abrupt millennial-scale climate changes: a statistical modelling study by Takahito Mitsui · Michel Crucifix

Checklist for your thesis evaluation

Evaluating your thesis ensures that your research meets the standards of academia. Here's an elaborate checklist to guide you through this critical process.

Content and structure

• Is the thesis statement clear, concise, and debatable?
• Does the introduction provide sufficient background and context?
• Is the literature review comprehensive, relevant, and well-organized?
• Does the methodology section clearly describe and justify the research methods?
• Are the results/findings presented clearly and logically?
• Does the discussion interpret the results in light of the research question and existing literature?
• Is the conclusion summarizing the research and suggesting future directions or implications?

Clarity and coherence

• Is the writing clear and free of jargon?
• Are ideas and sections logically connected and flowing?
• Is there a clear narrative or argument throughout the thesis?

Research quality

• Is the research question significant and relevant?
• Are the research methods appropriate for the question?
• Is the sample size (if applicable) adequate?
• Are the data analysis techniques appropriate and correctly applied?
• Are potential biases or limitations addressed?

Originality and significance

• Does the thesis contribute new knowledge or insights to the field?
• Is the research grounded in existing literature while offering fresh perspectives?

Formatting and presentation

• Is the thesis formatted according to institutional guidelines?
• Are figures, tables, and charts clear, labeled, and referenced in the text?
• Is the bibliography or reference list complete and consistently formatted?
• Are appendices relevant and appropriately referenced in the main text?

Grammar and language

• Is the thesis free of grammatical and spelling errors?
• Is the language professional, consistent, and appropriate for an academic audience?
• Are quotations and paraphrased material correctly cited?

Feedback and revision

• Have you sought feedback from peers, advisors, or experts in the field?
• Have you addressed the feedback and made the necessary revisions?

Overall assessment

• Does the thesis as a whole feel cohesive and comprehensive?
• Would the thesis be understandable and valuable to someone in your field?

Ensure to use this checklist to leave no ground for doubt or missed information in your thesis.

After writing your thesis, the next step is to discuss and defend your findings verbally in front of a knowledgeable panel. You’ve to be well prepared as your professors may grade your presentation abilities.

Preparing your thesis defense

A thesis defense, also known as "defending the thesis," is the culmination of a scholar's research journey. It's the final frontier, where you’ll present their findings and face scrutiny from a panel of experts.

Typically, the defense involves a public presentation where you’ll have to outline your study, followed by a question-and-answer session with a committee of experts. This committee assesses the validity, originality, and significance of the research.

The defense serves as a rite of passage for scholars. It's an opportunity to showcase expertise, address criticisms, and refine arguments. A successful defense not only validates the research but also establishes your authority as a researcher in your field.

Here’s how you can effectively prepare for your thesis defense .

Now, having touched upon the process of defending a thesis, it's worth noting that scholarly work can take various forms, depending on academic and regional practices.

One such form, often paralleled with the thesis, is the 'dissertation.' But what differentiates the two?

Dissertation vs. Thesis

Often used interchangeably in casual discourse, they refer to distinct research projects undertaken at different levels of higher education.

To the uninitiated, understanding their meaning might be elusive. So, let's demystify these terms and delve into their core differences.

Here's a table differentiating between the two.

Wrapping up

From understanding the foundational concept of a thesis to navigating its various components, differentiating it from a dissertation, and recognizing the importance of proper citation — this guide covers it all.

As scholars and readers, understanding these nuances not only aids in academic pursuits but also fosters a deeper appreciation for the relentless quest for knowledge that drives academia.

It’s important to remember that every thesis is a testament to curiosity, dedication, and the indomitable spirit of discovery.

Good luck with your thesis writing!

Frequently Asked Questions

A thesis typically ranges between 40-80 pages, but its length can vary based on the research topic, institution guidelines, and level of study.

A PhD thesis usually spans 200-300 pages, though this can vary based on the discipline, complexity of the research, and institutional requirements.

To identify a thesis topic, consider current trends in your field, gaps in existing literature, personal interests, and discussions with advisors or mentors. Additionally, reviewing related journals and conference proceedings can provide insights into potential areas of exploration.

The conceptual framework is often situated in the literature review or theoretical framework section of a thesis. It helps set the stage by providing the context, defining key concepts, and explaining the relationships between variables.

A thesis statement should be concise, clear, and specific. It should state the main argument or point of your research. Start by pinpointing the central question or issue your research addresses, then condense that into a single statement, ensuring it reflects the essence of your paper.

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In the vast landscape of academic inquiry, the hypothesis stands as a beacon of focus, a starting point that ignites the pursuit of knowledge. It is a pivotal element in the journey of every dissertation, a concise statement that encapsulates the essence of the research endeavour. A well-constructed hypothesis defines the research's purpose and sets the course for systematic investigation and analysis.

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In this article, we comprehensively explore the role and significance of the hypothesis, dissecting its various forms and illuminating its critical position in the research process. Whether you are a seasoned researcher or taking your first steps into the world of academia, the hypothesis remains a cornerstone, a guiding light that shapes the inquiry, fuels curiosity, and ultimately leads to the unveiling of new insights and understanding. Unlock the essence of a dissertation hypothesis with our blog post, clarifying 'What is a Hypothesis in a Dissertation' for your research journey.

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What is a Hypothesis in a Dissertation?

Hypotheses play a vital role in any study. How would you define the underlying belief of a dissertation? It serves as a response to the researcher's posed research question. Derived from the examined facts, it anticipates connections among them. After analyzing the selected research issue, the researcher develops initial notions about the relationships between known facts by addressing the most significant concerns.

Explore What is Hypothesis in Psychology? 

The research hypothesis gradually takes effect on this basis. Dissertation hypotheses are vital for scientific research: they guide study direction, enhance result prediction, inform data collection, and validate theories through experimentation.

Learn More About Research Hypothesis Here 

Expert Tip: Crafting an accurate hypothesis requires a firm grasp of the historical context and theoretical foundations within the field where the issue is situated.

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What are some characteristics of a dissertation hypothesis.

Here are some points to remember:

Sources that can be trusted should be included in it. It is your responsibility as an author to defend your position, so be sure you include testable assumptions and material that is available on the internet. The problem studied must be emphasized, and powerful statements should be delivered.

There should be no question about it. You should not turn a hypothesis into a rhetorical question that doesn't need an answer. Dissertation hypotheses are more positive than thesis statements since you need to research and draw conclusions.

There should not be too much complexity. It implies that your readers also understand the point if you make the dissertation hypothesis overt. Conversely, defending your position risks falling into the wordiness trap. Make a brief, precise idea that represents your concept, and check the word limit.

References and citations should be included. If there are no references, what is the point of a hypothesis? References and bibliographies that helped you make an advanced step in that area should be cited along with previous authors and problems studied earlier. Give credit to the researchers when you base your academic paper on their work.

Why is the Dissertation Hypothesis Important?

A research hypothesis is a prediction about the outcome of a study that can be tested. This includes variables and their relationships as well as aspects such as the population. Based on empirical evidence, it specifies the role played by each element. In conducting research, the researcher makes certain assumptions. As far as we know, the aim is to present the expected results after they are tested.

• This ensures the validity and scientific integrity of the entire research process.
• The probability of failure and progress in research can be assumed.
• As a result, it helps connect the underlying theory and the specific research question .
• In addition, it assists in data analysis and measures research validity and reliability.
• It proves the validity of the research by providing a basis or evidence.
• In contrast to theoretical descriptions, it helps to describe research studies in concrete terms.

Main Sources of Hypothesis

• Theoretical science.
• Observations based on previous studies and current experiences.
• Similarities between different phenomena.
• A general pattern of thinking that affects people.

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What are Some of the Types of Hypotheses?

Understanding the concept of hypotheses completely is essential for creating a good hypothesis. It is, therefore, important to understand the different types of hypotheses before you begin writing.

Regarding types, there are mainly two, namely Alternative and Null hypotheses.

1. Alternative Hypothesis

Academically, it is often referred to as H1. Identifying the expected outcome of your research procedure is the purpose of this type of research. Furthermore, it can be further classified into two subcategories:

The first is directional: This statement specifies how to gather the expected results. In most cases, it is used to establish a relationship between variables rather than to compare multiple groups. 

Example : The performance of athletes on the field will be improved by attending physiotherapy sessions.

Another alternative hypothesis is non-directional: As the name implies, a non-directional alternative hypothesis does not suggest a direction for the expected outcomes.

Example : Attending physiotherapy sessions affects an athlete's performance on the field.

Observe carefully the two statements in the above examples. A directional statement states that physiotherapy sessions will enhance or boost performance. Non-directional statements establish a correlation between the two variables (physiotherapy sessions and performance). There is no indication, however, that physiotherapy sessions will result in better or worse performance.

2. Null Hypothesis

H0 is the null hypothesis. As opposed to an alternative hypothesis, there is a null hypothesis. This is a statement that defines the opposite of what you expect to see during your research. Essentially, a null hypothesis asserts no relationship exists between the variables specified in the hypothesis.

The last example can be stated as follows to provide an idea of how a null hypothesis is written:

Example: On-field performance is not affected by physiotherapy sessions.

Alternative and null hypotheses are written to clarify and examine the research problem in detail. A research problem statement is a question that is not valid or testable, while a hypothesis is a hypothesis that can be tested. The former, however, can be tested, validated, or denied.

3. Simple Hypothesis

Statements that reflect the relationship between dependent and independent variables are called dependent statements. You will understand the example if you follow it through,

• Lung cancer is a common result of smoking
• Obesity may result from sugar-rich diets

4. Complex Hypothesis

Depending on the research problem, complex hypotheses imply a relationship between several dependent variables. You can better understand this by following the examples below:

• A person who eats more fruits tends to have a higher immunity, a lower cholesterol level, and a higher metabolic rate.
• Work hours can be made more productive by taking short breaks during them.

5. Empirical Hypothesis

A "Working Hypothesis" is also known as an experiment that validates a theory. As a result, the statement appears plausible and not just a wild guess.

In order to learn how to create an empirical hypothesis, here are a few examples:

• Anaemia is less likely to occur in people who take iron tablets than in ones who take vitamin B12.
• Giving food immediately after obedience to a command helps dogs learn faster.

6. Statistical Hypothesis

The statistical hypothesis is a statement claiming an explanation based on a sample of the population. An example of logic-based analysis is researching a particular population and gathering evidence using a certain sample size.

The following are some hypothetical statistical statements to help you understand how your research can be conducted by leveraging statistical data:

• 54% of Canadians are between the ages of 22 and 27
• An Agro-based industry in Kenya employs 57% of the rural population.

Hypothesis VS Prediction: How Do They Differ?

Hypotheses can mean predictions, too, causing confusion. Both are guesses but different. Hypotheses are for science; predictions are more common outside of science. A hypothesis is like a smart guess. It uses what we've learned to understand things we're unsure about. It connects facts to make an educated scientific guess. Experiments test these guesses. Assumptions are what you think will happen in the study.

Predictions are different. They're often just guesses without proof. While they could be scientific in theory, they usually lack facts. Predictions guess what might happen, often by people who don't know much. Also, how we prove these is very different. Predictions have only one test: if they're right or wrong when the event happens. Hypotheses can be tested many times by different scientists using various methods.

The following examples will help you better understand the difference between a hypothesis and a prediction:

Example Hypothesis: Eating more fruits and vegetables will lead to faster weight loss and a cleaner body.

The hypothesis is based on general knowledge (i.e. fruits and vegetables have fewer calories than other foods) and past experiences (i.e. people who choose healthier foods like fruits and vegetables lose more weight). Though it is still a guess, it is based on facts and can be tested.

Example Prediction: 2023 will be the end of the world.

A prediction foretells what will happen in the future. However, given the lack of actual grounded evidence to support this claim, it is a fictional assumption.

How to Develop a Hypothesis?

It is not easy to select a hypothesis. Let us suppose you have chosen an intriguing topic with lots of research potential. It may be difficult for you to choose a dissertation hypothesis in this case. When you have multiple exploration sources, it can be challenging to determine the hypothesis. These dissertation hypothesis writing guidelines/steps can help you improve the quality of your hypothesis part.

1. Conducting Research is the First Step

Before creating a hypothesis, a person should collect considerable information. You should read as much as you can on the topic under research; you should read books and articles by scientists, experts and professionals who have examined something related to your topic. By absorbing their knowledge and experience, you will be ready to conduct your own research. Gaining new facts may enable you to analyse other scholars' thoughts objectively and even criticize them after gaining new knowledge about your problem. You are ready to conduct research when there is something that has not been investigated about your topic. Make a list of what you would like to investigate.

2. Hypothesis Generation is the Second Step

It is now time to formulate a clear hypothesis based on your research. When a student chooses a research topic, they often have an idea of what they are going to investigate. At this step, many students fail to realize the problem of their research and need time to enhance their knowledge by reading clever thoughts by famous scholars. Each student must submit a dissertation proposal with a hypothesis or approximate thesis statement to be considered for a dissertation, but a true hypothesis is only presented after thorough research is conducted. Your hypothesis will be supported by the data and evidence you collect after you complete the investigation.

3. Supporting Your Hypothesis in Step Three

Half the success lies in brainstorming good concrete hypotheses that attract the reader's attention. You will be awarded a degree when your professor recognizes that your dissertation deserves to be read. To answer this rhetorical question, you asked at the start of the dissertation, you must support your hypothesis with reliable evidence. In other words, you should support your hypothesis throughout the dissertation in order to persuade the reader. Utilize the ideas of renowned scholars and experts in your field as well as your own research.

What are Some Tips and Tricks for Writing A Good Hypothesis?

Below are some tricks and tips for your convenience:

• For a dissertation hypothesis to be effective, you will need to design an experiment that will allow the data you collect to be analyzed statistically. Hypothesis testing begins with this step. It is possible to reject either type of hypothesis through statistical analysis. As the student, you will have to refine or redesign the research hypothesis if it is rejected.
• When formulating your dissertation hypothesis, conduct thorough research. Do not assume that your alternative and null hypotheses are true out of the blue. You have to believe them to be true. An effective dissertation hypothesis can be backed up by literature and research.
• The hypothesis should be simple enough for the reader to understand. As a writer, if your hypotheses are complicated, it can be hard to explain your ideas well. Know how many words you're expected to use for your hypothesis, and once you've got that, make sure it's short and to the point.
• Always make sure that the hypothesis is related to your paper's aims and objectives. This is crucial if you are making a vague or ambiguous claim. Moreover, the hypothesis should be linked with your research questions; through it, your readers should be able to get a clear sense of what you are trying to convey. You will be able to establish this relationship pretty well if you get dissertation help from a professional writer. In case you are too worried: do not be because you have firm support to rely on since we are experts at providing dissertation writing assistance.
• Remember that citations and references should also be included. In order to give credit where it is due, you should cite any work you used to develop your ideas. 

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Stating the Obvious: Writing Assumptions, Limitations, and Delimitations

During the process of writing your thesis or dissertation, you might suddenly realize that your research has inherent flaws. Don’t worry! Virtually all projects contain restrictions to your research. However, being able to recognize and accurately describe these problems is the difference between a true researcher and a grade-school kid with a science-fair project. Concerns with truthful responding, access to participants, and survey instruments are just a few of examples of restrictions on your research. In the following sections, the differences among delimitations, limitations, and assumptions of a dissertation will be clarified.

Delimitations

Delimitations are the definitions you set as the boundaries of your own thesis or dissertation, so delimitations are in your control. Delimitations are set so that your goals do not become impossibly large to complete. Examples of delimitations include objectives, research questions, variables, theoretical objectives that you have adopted, and populations chosen as targets to study. When you are stating your delimitations, clearly inform readers why you chose this course of study. The answer might simply be that you were curious about the topic and/or wanted to improve standards of a professional field by revealing certain findings. In any case, you should clearly list the other options available and the reasons why you did not choose these options immediately after you list your delimitations. You might have avoided these options for reasons of practicality, interest, or relativity to the study at hand. For example, you might have only studied Hispanic mothers because they have the highest rate of obese babies. Delimitations are often strongly related to your theory and research questions. If you were researching whether there are different parenting styles between unmarried Asian, Caucasian, African American, and Hispanic women, then a delimitation of your study would be the inclusion of only participants with those demographics and the exclusion of participants from other demographics such as men, married women, and all other ethnicities of single women (inclusion and exclusion criteria). A further delimitation might be that you only included closed-ended Likert scale responses in the survey, rather than including additional open-ended responses, which might make some people more willing to take and complete your survey. Remember that delimitations are not good or bad. They are simply a detailed description of the scope of interest for your study as it relates to the research design. Don’t forget to describe the philosophical framework you used throughout your study, which also delimits your study.

Limitations

Limitations of a dissertation are potential weaknesses in your study that are mostly out of your control, given limited funding, choice of research design, statistical model constraints, or other factors. In addition, a limitation is a restriction on your study that cannot be reasonably dismissed and can affect your design and results. Do not worry about limitations because limitations affect virtually all research projects, as well as most things in life. Even when you are going to your favorite restaurant, you are limited by the menu choices. If you went to a restaurant that had a menu that you were craving, you might not receive the service, price, or location that makes you enjoy your favorite restaurant. If you studied participants’ responses to a survey, you might be limited in your abilities to gain the exact type or geographic scope of participants you wanted. The people whom you managed to get to take your survey may not truly be a random sample, which is also a limitation. If you used a common test for data findings, your results are limited by the reliability of the test. If your study was limited to a certain amount of time, your results are affected by the operations of society during that time period (e.g., economy, social trends). It is important for you to remember that limitations of a dissertation are often not something that can be solved by the researcher. Also, remember that whatever limits you also limits other researchers, whether they are the largest medical research companies or consumer habits corporations. Certain kinds of limitations are often associated with the analytical approach you take in your research, too. For example, some qualitative methods like heuristics or phenomenology do not lend themselves well to replicability. Also, most of the commonly used quantitative statistical models can only determine correlation, but not causation.

Assumptions

Assumptions are things that are accepted as true, or at least plausible, by researchers and peers who will read your dissertation or thesis. In other words, any scholar reading your paper will assume that certain aspects of your study is true given your population, statistical test, research design, or other delimitations. For example, if you tell your friend that your favorite restaurant is an Italian place, your friend will assume that you don’t go there for the sushi. It’s assumed that you go there to eat Italian food. Because most assumptions are not discussed in-text, assumptions that are discussed in-text are discussed in the context of the limitations of your study, which is typically in the discussion section. This is important, because both assumptions and limitations affect the inferences you can draw from your study. One of the more common assumptions made in survey research is the assumption of honesty and truthful responses. However, for certain sensitive questions this assumption may be more difficult to accept, in which case it would be described as a limitation of the study. For example, asking people to report their criminal behavior in a survey may not be as reliable as asking people to report their eating habits. It is important to remember that your limitations and assumptions should not contradict one another. For instance, if you state that generalizability is a limitation of your study given that your sample was limited to one city in the United States, then you should not claim generalizability to the United States population as an assumption of your study. Statistical models in quantitative research designs are accompanied with assumptions as well, some more strict than others. These assumptions generally refer to the characteristics of the data, such as distributions, correlational trends, and variable type, just to name a few. Violating these assumptions can lead to drastically invalid results, though this often depends on sample size and other considerations.

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What is a Hypothesis?

Table of Contents

Defining the hypothesis, the role of a hypothesis in the scientific method, types of hypotheses, hypothesis formulation, hypotheses and variables.

• The Importance of Testing Hypotheses
• The Hypothesis and Sociological Theory

In sociology, as in other scientific disciplines, the hypothesis serves as a crucial building block for research. It is a central element that directs the inquiry and provides a framework for testing the relationships between social phenomena. This article will explore what a hypothesis is, how it is formulated, and its role within the broader scientific method. By understanding the hypothesis, students of sociology can grasp how sociologists construct and test theories about the social world.

A hypothesis is a specific, testable statement about the relationship between two or more variables. It acts as a proposed explanation or prediction based on limited evidence, which researchers then test through empirical investigation. In essence, it is a statement that can be supported or refuted by data gathered from observation, experimentation, or other forms of systematic inquiry. The hypothesis typically takes the form of an “if-then” statement: if one variable changes, then another will change in response.

In sociological research, a hypothesis helps to focus the investigation by offering a clear proposition that can be tested. For instance, a sociologist might hypothesize that an increase in education levels leads to a decrease in crime rates. This hypothesis gives the researcher a direction, guiding them to collect data on education and crime, and analyze the relationship between the two variables. By doing so, the hypothesis serves as a tool for making sense of complex social phenomena.

The hypothesis is a key component of the scientific method, which is the systematic process by which sociologists and other scientists investigate the world. The scientific method begins with an observation of the world, followed by the formulation of a question or problem. Based on prior knowledge, theory, or preliminary observations, researchers then develop a hypothesis, which predicts an outcome or proposes a relationship between variables.

Once a hypothesis is established, researchers gather data to test it. If the data supports the hypothesis, it may be used to build a broader theory or to further refine the understanding of the social phenomenon in question. If the data contradicts the hypothesis, researchers may revise their hypothesis or abandon it altogether, depending on the strength of the evidence. In either case, the hypothesis helps to organize the research process, ensuring that it remains focused and methodologically sound.

In sociology, this method is particularly important because the social world is highly complex. Researchers must navigate a vast range of variables—age, gender, class, race, education, and countless others—that interact in unpredictable ways. A well-constructed hypothesis allows sociologists to narrow their focus to a manageable set of variables, making the investigation more precise and efficient.

Sociologists use different types of hypotheses, depending on the nature of their research question and the methods they plan to use. Broadly speaking, hypotheses can be classified into two main types: null hypotheses and alternative (or research) hypotheses.

Null Hypothesis

The null hypothesis, denoted as H0, states that there is no relationship between the variables being studied. It is a default assumption that any observed differences or relationships are due to random chance rather than a real underlying cause. In research, the null hypothesis serves as a point of comparison. Researchers collect data to see if the results allow them to reject the null hypothesis in favor of an alternative explanation.

For example, a sociologist studying the relationship between income and political participation might propose a null hypothesis that income has no effect on political participation. The goal of the research would then be to determine whether this null hypothesis can be rejected based on the data. If the data shows a significant correlation between income and political participation, the null hypothesis would be rejected.

Alternative Hypothesis

The alternative hypothesis, denoted as H1 or Ha, proposes that there is a significant relationship between the variables. This is the hypothesis that researchers aim to support with their data. In contrast to the null hypothesis, the alternative hypothesis predicts a specific direction or effect. For example, a researcher might hypothesize that higher levels of education lead to greater political engagement. In this case, the alternative hypothesis is proposing a positive correlation between the two variables.

The alternative hypothesis is the one that guides the research design, as it directs the researcher toward gathering evidence that will either support or refute the predicted relationship. The research process is structured around testing this hypothesis and determining whether the evidence is strong enough to reject the null hypothesis.

The process of formulating a hypothesis is both an art and a science. It requires a deep understanding of the social phenomena under investigation, as well as a clear sense of what is possible to observe and measure. Hypothesis formulation is closely linked to the theoretical framework that guides the research. Sociologists draw on existing theories to generate hypotheses, ensuring that their predictions are grounded in established knowledge.

To formulate a good hypothesis, a researcher must identify the key variables and determine how they are expected to relate to one another. Variables are the factors or characteristics that are being measured in a study. In sociology, these variables often include social attributes such as class, race, gender, age, education, and income, as well as behavioral variables like voting, criminal activity, or social participation.

For example, a sociologist studying the effects of social media on self-esteem might propose the following hypothesis: “Increased time spent on social media leads to lower levels of self-esteem among adolescents.” Here, the independent variable is the time spent on social media, and the dependent variable is the level of self-esteem. The hypothesis predicts a negative relationship between the two variables: as time spent on social media increases, self-esteem decreases.

A strong hypothesis has several key characteristics. It should be clear and specific, meaning that it unambiguously states the relationship between the variables. It should also be testable, meaning that it can be supported or refuted through empirical investigation. Finally, it should be grounded in theory, meaning that it is based on existing knowledge about the social phenomenon in question.

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Answered By: APUS Librarians Last Updated: Apr 15, 2022     Views: 129602

Both the hypothesis statement and the thesis statement answer a research question. 

• A hypothesis is a statement that can be proved or disproved. It is typically used in quantitative research and predicts the relationship between variables.  
• A thesis statement is a short, direct sentence that summarizes the main point or claim of an essay or research paper. It is seen in quantitative, qualitative, and mixed methods research. A thesis statement is developed, supported, and explained in the body of the essay or research report by means of examples and evidence.

Every research study should contain a concise and well-written thesis statement. If the intent of the study is to prove/disprove something, that research report will also contain a hypothesis statement.

NOTE: In some disciplines, the hypothesis is referred to as a thesis statement! This is not accurate but within those disciplines it is understood that "a short, direct sentence that summarizes the main point" will be included.

For more information, see The Research Question and Hypothesis (PDF file from the English Language Support, Department of Student Services, Ryerson University).

How do I write a good thesis statement?

How do I write a good hypothesis statement?

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Dissertations and Theses: A Finding Guide: Cornell Dissertation Guidelines

• Introduction
• Cornell Theses
• Non-Cornell Theses
• Open Access, etc.
• Cornell Dissertation Guidelines

Cornell Dissertations Guidelines

General guidance on dissertations and theses is available from the Cornell University Graduate School Thesis & Dissertation web page . For more detailed guidance, see Guide on Writing Your Thesis/Dissertation .

Note that in the Bibliography (or References or Works Cited) section of the Required Sections, Guidelines, and Suggestions page , the following advice is offered.

Required? Yes.

• A bibliography, references, or works cited is required for your thesis or dissertation. Please conform to the standards of leading academic journals in your field.
• As a page heading, use “BIBLIOGRAPHY” (or “REFERENCES” or “WORKS CITED”) in all capital letters, centered on the page. The bibliography should always begin on a new page. Bibliographies may be single-spaced within each entry but should include 24 points of space between entries.
• << Previous: Open Access, etc.
• Next: Help >>
• Last Updated: Sep 18, 2024 1:17 PM
• URL: https://guides.library.cornell.edu/dissertations

PSU's Three-Minute-Thesis Winner Moves Forward To National Competition

by Lacey Friedly September 19th 2024 Share

Anne Johnson, a Portland State University doctoral candidate in sociology, will compete in the Council of Graduate Schools' national Three-Minute Thesis competition in St. Louis this year after winning regionals in March.

The Three-Minute Thesis contest, or 3MT for short, is a research communication competition designed to help graduate students develop presentation skills by consolidating their research and presenting it succinctly to a non-specialist audience, all in just three minutes.

Johnson was awarded first place in the PSU 3MT competition last November and took home a cash prize of $1,000. She went on to take first prize in the regional competition, too, held virtually as part of the Western Association of Graduate Schools' annual meeting on March 22. Her doctoral dissertation draws on both medical sociology and criminology, examining phlebotomy, blood draws, in two contexts: medicine and law enforcement.

This December, Johnson will compete in the national round of 3MT, held at the Council of Graduate Schools (CGS) 2024 Annual Meeting in St. Louis, Missouri.

The 2024 PSU Three Minute Thesis competition will be held on campus November 14, 2024. The Graduate School will accept applications starting Friday, October 4, through midnight on Sunday, October 20. Learn more and sign up for the competition here.

Note: You must be logged in to your Odin account to view the site. If you are logged in and still cannot view it, contact Lisa Sablan .

NOT JUST A POKE: EXPLORING THE SOCIAL SIGNIFICANCE OF DRAWING BLOOD

Most of us are familiar with having blood drawn at the doctor’s office, but police officers are increasingly using blood draws as a form of chemical testing for suspected impaired drivers. Johnson's work explores phlebotomy in both medical and law enforcement contexts.

"I think too often in medical spaces, and in general, we do not give enough weight to how emotionally significant blood draws can be for people," Johnson said. "Phlebotomy is the most common invasive medical procedure. But in talking to people about their experiences getting blood drawn, it is more significant than we often realize. People hold on to the trauma of bad blood draws and remember them. So there's room culturally right now for us to think about it."

For her three-paper dissertation, Johnson interviewed patients who had had their blood drawn in purely medical contexts, as well as law enforcement officers who draw blood from drivers. In the medical context, many participants who have had negative experiences with blood draws reported avoiding medical care so as to not undergo more blood draws. What’s more, many of her participants reported feeling dehumanized by their phlebotomists’ focus on efficiency instead of care. In the policing context, efficiency is the chief motivator for law enforcement phlebotomy: when officers can draw blood themselves, they save time and money, as well as avoid clashes with medical providers over non-consensual blood draws. The tension between efficiency and care is a throughline of her dissertation.

Johnson hopes that her findings will be valuable to medical professionals in search of ways to improve patient experiences, sociologists interested in power dynamics, and members of the law enforcement and legal community as police-conducted blood draws continue to spread across the United States.

"The culminating idea of my 3MT speech is that most people don't know law enforcement phlebotomy is happening. If a community decides, 'Our DUI situation is severe enough that yes, we want our police officers to be drawing blood,' then that decision may be the right choice for that community. But most of the officers I interviewed said that their community members have no idea that police can draw blood until they’re pulled over. I would like it to be more of a public conversation," Johnson said.

WHY DO 3MT?

The 3MT concept was developed by the University of Queensland in 2008 and is now held at over 900 universities worldwide. The idea is to challenge graduate students to be as succinct and engaging as possible when communicating complex research topics, which helps develop their presentation and research communication skills.

"When I had to distill my dissertation topic into three minutes, it really helped me tighten up the language. It prepared me with an elevator pitch that I can now give to potential employers or share with other academics at conferences. Having to focus in on the message I want to communicate was really helpful," Johnson said.  

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The Conflict Thesis Reimagined: From Theological Reform to Secular Weapon

by James Ungureanu September 17, 2024

R ecent scholarship on the so-called conflict between science and religion has revisited the reception of John William Draper’s History of the Conflict Between Religion and Science (1875) and Andrew Dickson White’s A History of the Warfare of Science with Theology in Christendom (1896). [1] Indeed, contrary to common perception, Draper and White did not frame science and religion as inherently antagonistic; their positions were far more complex and nuanced.

This complexity is reflected in the diverse public responses to their works, where three predominant patterns emerge. [2] First, the more liberal press heralded Draper and White’s narratives as facilitating a “new Reformation.” They viewed the conflict rhetoric as instrumental in advocating for a distinction between religion and theology, and as a necessary step towards aligning faith with modernity.

In contrast, orthodox religious critics found such separation untenable. For them, faith was inseparable from doctrinal foundations, and they regarded Draper and White’s approach as a direct threat to Christianity, condemning their works as historically inaccurate and ideologically dangerous.

Meanwhile, secularists and atheists appropriated Draper and White’s conflict thesis to advance their own agendas. They interpreted it as an indictment of all religious belief, deploying the language of conflict to erode faith entirely, while finding it paradoxical that Draper and White themselves retained religious convictions.

In retrospect, the anxieties of conservative critics were not entirely misplaced. Here I will investigate how early twentieth-century skeptics appropriated and transformed the conflict thesis into a more secular narrative, significantly broadening its influence.

Organized Freethought in Victorian England

Liberal Protestantism, emerging from the Enlightenment and Romanticism, sought to align religion with contemporary values and scientific understanding. However, this modernization often led to a deeper questioning of religion’s relevance. As James Turner noted, religion was increasingly humanized, making it feasible “to abandon God, to believe simply in man.” [3]

While liberal Protestants adapted their faith, skeptics doubted whether religion retained any substantive value. Leslie Stephen, for instance, critiqued Matthew Arnold’s idea of preserving a “sublimated essence of theology,” questioning whether aesthetic judgments could sustain religious belief in the absence of doctrinal foundations. [4] By the late nineteenth century, these theological concessions helped pave the way for organized secularism to gain societal respectability.

Victorian freethought inherited diverse traditions, particularly the Enlightenment’s commitment to reason and Deist principles. In mid-nineteenth-century England, “secularism” emerged as a philosophical movement, deeply influenced by Thomas Paine’s The Age of Reason (1793). Paine denounced the church as enslaving humanity, advocating for faith in reason and a “Religion of Humanity.” His critique of the Bible as inconsistent and mythological laid the groundwork for radical freethought.

Freethought, tracing its roots to English Deists, found resonance with the Protestant Reformation’s spirit of liberating religious thought from clerical authority. [5] Figures like Richard Carlile, Robert Taylor, Robert Owen, and Charles Southwell were key advocates of freethought, pushing for self-improvement, education, and reform. Carlile, imprisoned for reprinting Paine’s The Age of Reason , saw the printing press as a tool to dismantle the “double yoke” of “Kingcraft and Priestcraft,” using publications to rally against religious and political institutions.

As public opinion grew more tolerant and English society became more stable, freethinkers adopted a less combative stance. By mid-century, leading figures institutionalized irreligion on an unprecedented scale, shifting from radical opposition to a broader, more accepted promotion of secularism.

The Rise of Radical Freethought in the Late Nineteenth Century

The late nineteenth century marked a golden age for radical freethought, during which freethinkers celebrated the liberation of humanity from religious constraints. This movement, led by figures such as George Jacob Holyoake, Charles Bradlaugh, Robert G. Ingersoll, and Joseph M. McCabe, extended its influence across both urban and rural areas through tracts, pamphlets, and magazines.

Interestingly, many freethinkers came from liberal Protestant backgrounds. Scholars like Leigh Eric Schmidt and Christopher Grasso have highlighted the complex relationship between American Protestantism and secularism. [6] For instance, Robert Ingersoll, raised by a liberal Presbyterian minister, eventually favored science over religious belief. Similarly, Samuel P. Putnam’s rejection of theism was shaped by liberal religious ideas from figures like Channing and Emerson. Many American Protestants, navigating from liberalism to infidelity, demonstrated the intersection of Protestantism and secularism, revealing a matrix of rivalry, alliance, and opposition.

In Britain, secularism advanced through both secularists and agnostics. As Bernie Lightman observed, while Thomas H. Huxley used agnosticism to distance himself from atheism, secularists increasingly employed the term to articulate atheistic views. Yet secularists recognized the influence of thinkers like Spencer, Huxley, and Tyndall, even as they criticized agnostics and religious liberals for compromising with religion.

Foote’s Freethinker magazine ridiculed agnostics who attended church, and Bradlaugh condemned figures like Huxley and Spencer for “intellectual vacillation” in failing to promote materialism fully. [7] Darwin, too, faced Bradlaugh’s criticism for what was seen as pandering to religious norms, especially in securing his place in Westminster Abbey. [8]

Ultimately, figures like Bradlaugh were perplexed by agnostics who, in their view, remained too closely tied to religious traditions.

Responses from Agnostics and the Evolving Secularist Landscape

Agnostics often responded to critiques with sharp rebuttals. Thomas Huxley, a leading figure in the agnostic movement, expressed disdain for certain elements within the freethought community. He criticized much of its literature, dismissing what he saw as “heterodox ribaldry,” which he found more distasteful than orthodox fanaticism. Huxley argued that attacking Christianity with scurrilous rhetoric was counterproductive, particularly in England, where such methods were outdated. He harbored a “peculiar abhorrence” for Charles Bradlaugh and his associates.

Bernie Lightman has demonstrated that Huxley and his scientific naturalist peers were repelled by Bradlaugh’s coarse atheism. [9] In correspondence with agnostic Richard Bithell, Huxley declined to support Charles Watts, criticizing freethought literature as repetitive and tiresome. He lamented how such works alienated thoughtful readers, noting: “It is monstrous that I cannot let one of these professed organs of Freethought lie upon my table without someone asking if I approve of this réchauffé of Voltaire or Paine.” [10]

Even moderate freethinkers like George Jacob Holyoake faced discrimination from agnostics. Although Holyoake and Herbert Spencer were longtime friends, Spencer refused Holyoake’s proposal to travel together to America in 1882, fearing it would be seen as an endorsement of Holyoake’s ideas.

Despite this, Holyoake remained a central figure among secularists. Raised in a religious household, his path led him through Christian denominations and eventually to freethought and naturalism. Holyoake often referenced his Christian upbringing to bolster his credibility as a freethinker, using his religious past to enhance his standing as a critic of religion. [11]

Holyoake’s Secularism and Its Impact

During his studies, George Jacob Holyoake encountered Robert Owen’s teachings and joined the Owenite movement as a “social missionary.” By 1843, he had taken over The Oracle of Reason and later founded The Reasoner and Herald of Progress , which became one of the longest-running freethought publications. Throughout the 1850s, Holyoake traveled widely, advocating for social reform and engaging in debates with religious opponents.

In 1849, Holyoake designated The Reasoner as “secular,” and in 1851, coined the term “secularism” to describe his freethought philosophy. He saw secularism as focused on this life, differentiating it from atheism by attracting theists and deists while avoiding the negative connotations of atheism. Holyoake’s secularism centered on social reform rather than religious critique, arguing that salvation, if it existed, was achieved through works, not faith. By promoting secularism, Holyoake sought collaboration with Christian liberals to advance rational morality.

In 1855, Holyoake and his brother Austin established a printing house on Fleet Street to distribute secularist literature. As president of the London Secular Society, Holyoake first met Charles Bradlaugh. Unlike Bradlaugh, Holyoake advocated cooperation among unbelievers, deists, and liberal theists to promote social reform, encouraging atheists to collaborate with liberal clergy to bridge the gap between secularists and Christian liberals.

The Watts Legacy and Secular Propaganda

Most importantly, George Jacob Holyoake’s conciliatory approach to secularism was embraced by Charles Watts and his son, Charles Albert Watts. In 1884, Charles Albert took a significant step toward consolidating secularist efforts by publishing the Agnostic Annual , marking a shift toward greater coordination within the secular movement.

The story of the Watts family’s contribution to freethought is well-documented. [12] Charles Watts, originally a Wesleyan minister’s son, became involved with Bradlaugh’s National Reformer before distancing himself after the “Knowlton affair” and aligning with Holyoake’s ethical humanism. By the 1880s, he took over Austin Holyoake’s printing firm and became a leading rationalist publisher. He eventually left the business to his son, Charles Albert, who sought to attract middle-class unbelievers by promoting agnosticism through the Agnostic Annual . Despite an incident where Huxley publicly disavowed any connection to the Annual, Charles Albert’s relationships with scientific naturalists remained intact.

Charles Albert expanded his efforts by publishing The Agnostic and establishing the “Agnostic Temple” in 1885, offering literature and holding meetings grounded in Spencer’s ideas. That same year, he launched Watts’s Literary Guide , a monthly publication catering to working-class and lower-middle-class audiences. The Guide , which eventually became the New Humanist , featured works from notable figures like Spencer, Huxley, Darwin, and Draper, often depicting the conflict between theology and science in dramatic terms.

Charles Albert also established the Propagandist Press Committee to further the distribution of rationalist literature, successfully expanding both the subscriber base and the visibility of secular publications.

Charles Albert Watts and the Rationalist Press Association

By the late nineteenth century, Charles Albert Watts had founded Watts & Co., and in 1899, his group of rationalists formed the Rationalist Press Association (RPA). Evolving from the Propagandist Press Committee, the RPA sought to promote freedom of thought in ethics, theology, and philosophy while advocating secular education and challenging traditional religious creeds. The RPA published books on religion, biblical criticism, and intellectual progress, emphasizing the perceived conflict between science and religion and advocating secular moral instruction.

The RPA featured works from key figures like Joseph McCabe and John M. Robertson. McCabe, a former Jesuit and prolific author, predicted the downfall of Christianity through scientific naturalism and biblical criticism. His Biographical Dictionary of Modern Rationalists celebrated Draper and White, though he acknowledged that both were theists. McCabe viewed Draper’s work as rationalist literature and praised White’s contribution to rationalism while noting his aim to purify, rather than destroy, Christianity. [13]

John M. Robertson, in his History of Freethought in the Nineteenth Century (1929), referred to Draper’s Intellectual Development as a key contribution to rationalist culture. He argued that Draper’s theism was likely a result of social pressure but acknowledged the naturalistic approach in his work. [14] Other secularists like Joseph Mazzini Wheeler and Samuel P. Putnam similarly recognized Draper and White as freethinkers, with Putnam seeing the Reformation as a precursor to the eventual decline of Protestantism and Roman Catholicism. [15]

In the early twentieth century, the RPA expanded its influence by reprinting “Rationalist classics” using mass-production techniques. Charles Albert Watts collaborated with publishers like Macmillan to produce affordable editions of influential works, distributing six-penny editions of texts by authors such as Darwin, Huxley, Spencer, Paine, and notably Draper and White. Draper’s work, which he saw as a preface to a broader departure from “the faith of the fathers,” was integral to the RPA’s mission to reach a wider audience with rationalist ideas.

Origins of American Freethought

The roots of American freethought trace back to Thomas Paine, whose influence remains foundational. Freethought, as a movement, challenges established beliefs and seeks knowledge, empowering citizens to discern truth and strengthen democracy. Freethinkers advocate reason over passion or outdated customs, overlapping with rationalism, secularism, and skepticism.

Paine’s Common Sense (1776) electrified America and became a rallying cry for revolution. His later works, The Rights of Man (1791) and The Age of Reason (1794), more directly engaged with freethought, with The Age of Reason launching a bold attack on organized religion. Declaring himself a deist, Paine famously stated, “my own mind is my own church.” For his views, he was censored, ridiculed, and ostracized upon his return to America. Even Thomas Jefferson distanced himself. Paine died in 1809, nearly forgotten, his funeral attended by only a few. It was only after the Civil War that freethought gained new life in the U.S.

Secularism, though less organized than in Britain, grew in prominence after the Civil War. James Turner notes that agnosticism emerged as a self-sustaining phenomenon within twenty years. [16] Robert G. Ingersoll, known as the “Great Agnostic,” became the chief exponent of this movement, leading the “Golden Age of Freethought” (1875–1914). Ingersoll’s oratory revived Paine’s tarnished reputation, defending his legacy in essays like Vindication of Thomas Paine (1877). Ingersoll himself opposed religion, which cost him his political career, though he diverged from Paine on issues like socialism. [17]

Ingersoll’s freethought views were complex. Though the son of a minister, he grew to abhor religion, and this stance cost him his political career, which ended while he was still in his twenties. His story reflects the broader challenge faced by the freethought movement, which struggled to gain mainstream acceptance. A mere accusation of being anti-religious could destroy a political candidate’s chances. Ingersoll himself opposed socialism, diverging from some of Paine’s more progressive ideas.

Ingersoll’s death in 1899 marked the end of an era. Unlike Paine, he was neither poor nor forgotten, and even his critics admired his eloquence and ability to connect with audiences across the social spectrum.

Freethinkers Respond to Draper

Freethinkers like Joseph Treat and T. D. Hall seized upon Draper’s History of the Conflict Between Religion and Science as a powerful tool in their efforts to promote secularism and challenge Christianity. Treat, in correspondence with Draper, argued that Christianity had consistently hindered genuine scientific inquiry. He praised Draper’s work for exposing this historical antagonism, asserting that Draper had liberated science from the “bondage” of Christian influence.

Hall, in his pamphlet Can Christianity Be Made to Harmonize with Science? , echoed Treat’s appreciation of Draper’s clarity but critiqued him for stopping short of declaring an outright incompatibility between science and Christianity. Hall insisted that Draper lacked the boldness to acknowledge Christianity’s inevitable collapse in the face of scientific progress. Once Christianity’s central doctrines—such as the Fall, Atonement, and Resurrection—were stripped away, Hall believed, the religion would unravel entirely.

These voices were part of a broader American freethought movement, led by publications like Truth Seeker , founded by D. M. Bennett in 1876. Truth Seeker and groups like the National Liberal League united freethinkers, rationalists, and religious skeptics in advocating for the complete secularization of society.

Across the Atlantic, Draper’s narrative also resonated with British freethinkers, particularly through Charles Albert Watts and the Rationalist Press Association. Watts, via his Watts’s Literary Guide (later New Humanist ), treated Draper’s work as a cornerstone for promoting secularism and rationalism. The Rationalist Press Association published works that undermined traditional religious views, with prominent figures like John M. Robertson and Joseph M. Wheeler consistently citing Draper’s analysis to support their campaigns for secular education and religious criticism.

For Robertson, Draper’s naturalistic outlook made his work indispensable to the freethought movement, despite Draper’s own theological leanings. Similarly, Wheeler and Samuel P. Putnam integrated Draper’s arguments into their broader critiques of religion, using his historical analysis not merely as a chronicle of science but as a potent tool in the battle to free society from religious dominance.

Freethinkers on both sides of the Atlantic adopted Draper’s narrative to legitimize their belief in the fundamental incompatibility of science and religion. Through their publications, organizations, and correspondence, they transformed Draper’s work into a weapon for advancing a secular society, one free from the influence of religious institutions.

Freethinkers Respond to White

Freethinkers, as they did with Draper, appropriated Andrew Dickson White’s A History of the Warfare of Science with Theology in Christendom to further their secular agenda. Publications like The American Free Thought Magazine praised White’s work for illustrating the historical struggle to modernize Christian theology, framing it as a triumph of science over religious dogma. The magazine argued that White’s history was essential for any freethinker’s library, not merely for cataloging religious errors but for celebrating science’s victories.

In England, thinkers like Alfred W. Benn placed White alongside luminaries such as Buckle, Draper, and Lecky. However, Benn expressed frustration with White’s reluctance to fully reject Christianity, arguing that his conclusions logically pointed to the abandonment of its doctrines. For Benn and others, White’s work symbolized the deepening conflict between rational thought and religious belief.

White’s work also drew criticism from prominent atheists like Edward Payson Evans and Elizabeth Edson Gibson Evans. They were perplexed by White’s attempts to reconcile religion and science. Elizabeth criticized White’s refusal to fully disbelieve in religion, insisting that science had consistently debunked religious claims. Edward accused White of being overly generous to religion, contending that the conflict between science and faith was irreconcilable.

This tension was further evident in White’s interactions with Robert G. Ingersoll, the renowned agnostic orator. While Ingersoll appreciated White’s contribution to intellectual openness and his critique of religious authority, he saw White’s lingering religious sentiment as unnecessary. Ingersoll dismissed Christianity as not worth saving, sarcastically asking why God would make truth-seeking safe now after allowing it to be dangerous for centuries.

Despite White’s reluctance to fully embrace secularism, freethinkers eagerly adopted his work to undermine religious institutions. Charles Albert Watts, a prominent British secularist, published extensive reviews of White’s book in the Watts’s Literary Guide , encouraging White to write for the secularist Annual . Although White declined, secularists continued to use his work to advance their cause.

White himself was unsettled by this reception. He had aimed to provide a balanced critique, addressing both religious “scoffers” like Ingersoll and the religious “gush” of figures like John Henry Newman. In private, he expressed to his secretary George Lincoln Burr that he sought to present “the truth as it is in Jesus,” but both religious and irreligious readers often misinterpreted his work as an attack on faith itself.

In conclusion, while White’s intentions were more conciliatory than Draper’s, freethinkers and secularists embraced his narrative as part of their broader efforts to secularize society. Regardless of White’s personal beliefs, his work became a cornerstone in the intellectual campaign to discredit religious authority and advance rationalism.

Joseph McCabe and the “Land of Bunk”

One of the most significant secularists to appropriate Draper and White’s conflict thesis was Joseph McCabe, a former Franciscan monk turned outspoken atheist. McCabe believed that science and technology would not only solve society’s problems but also lead to a more rational and egalitarian world. His translation of Ernst Haeckel’s The Riddle of the Universe (1900) introduced Haeckel’s ideas to English-speaking audiences, and despite McCabe’s lack of formal scientific training, this association lent authority to his writings. A prolific author, McCabe produced over 200 books on science, history, and religion, championing evolutionary thought and forecasting Christianity’s inevitable demise in the face of modern science.

McCabe’s personal journey mirrored his intellectual transformation. Raised in a Franciscan monastery, where he took the name Brother Antony, McCabe was tormented by doubts about Christianity. His experiences in the monastery, marked by physical suffering and intellectual conflict, eventually led him to leave the priesthood in 1895. His account, Twelve Years in a Monastery (1897), detailed his disillusionment with the Church and marked his formal break with religion. From that point on, McCabe became a relentless advocate for atheism, insisting that science, not religion, held the answers to life’s great questions.

McCabe’s partnership with Kansas-based publisher Emanuel Haldeman-Julius was one of the defining collaborations of his career. Haldeman-Julius, known for his “Little Blue Books” series, provided affordable and accessible literature on topics ranging from politics to science. McCabe became the series’ most prolific contributor, writing 134 Little Blue Books and over 100 Big Blue Books . Haldeman-Julius praised McCabe as “the greatest scholar in the world,” crediting his works with advancing humanity’s cultural progress.

This partnership gave McCabe a renewed sense of purpose, especially after facing personal and professional setbacks in Britain. By 1925, after separating from his wife and severing ties with key British publishers, McCabe found both financial stability and intellectual validation through his collaboration with Haldeman-Julius. Over the following years, McCabe produced an immense body of work, earning substantial income while continuing to challenge religious orthodoxy.

One of McCabe’s most influential works, The Conflict Between Science and Religion (1927), essentially echoed Draper’s narrative but with a tone of triumph. McCabe confidently predicted that future historians would regard the denial of the science-religion conflict as laughable. He argued that “science has, ever since its birth, been in conflict with religion,” with Christianity as its “most deadly opponent.”

McCabe’s critique extended beyond traditional religious beliefs. He reserved particular scorn for modernist and liberal theologians, dismissing their attempts to reconcile Christianity with science as “the veriest piece of bunk that Modernism ever invented.” In McCabe’s view, rejecting Christianity’s core doctrines—whether through scientific reinterpretation or otherwise—was tantamount to rejecting Christianity entirely. For him, “progressive religion” was a contradiction, and those who embraced it were deluding themselves.

Ironically, McCabe used arguments similar to those of conservative Christians, accusing liberal theologians like Shailer Mathews of undermining Christianity’s foundations. He argued that attempts to reconcile science with religion were futile, given that science operated as a unified field while religion had never achieved such coherence. McCabe quipped that applying science to religion would require addressing “three hundred different collections of religious beliefs,” making any reconciliation impossible.

In McCabe’s final analysis, whether one adhered to orthodox Christianity or its modernist variants, the conflict with science was inevitable. He contended that modernists, in reducing God to abstractions like “Cosmic Force” or “Vital Principle,” had gutted religion of any meaningful content. Both fundamentalists and modernists, McCabe concluded, inhabited the same “land of bunk,” unable to recognize the inherent incompatibility between science and religion.

Emanuel Haldeman-Julius and the Philosophy of the “Little Blue Books”

Emanuel Haldeman-Julius, later known as the “Henry Ford of publishing,” was born to Jewish immigrants in Philadelphia and grew up in a secular household. Though his formal education ended in the eighth grade, his passion for reading and self-education shaped his early worldview. Influenced by thinkers like Omar Khayyam, Voltaire, and Robert Ingersoll, he developed a deep rejection of religion, identifying as a materialist and dismissing the notion of an afterlife. His early exposure to cheap pamphlets like The Rubaiyat and The Ballad of Reading Gaol ignited his desire to make literature accessible to the masses.

In 1915, Haldeman-Julius moved to Girard, Kansas, where he worked for the socialist newspaper Appeal to Reason . After marrying Annie Haldeman, niece of social reformer Jane Addams, he purchased the paper and began distributing pamphlets, marking the beginning of his publishing empire. His vision of providing affordable, pocket-sized booklets on a wide range of topics took shape in the Little Blue Books series, which covered literature, philosophy, science, and religion, and initially sold for just five cents. These pamphlets aimed to provide a “university in print” for working- and middle-class readers, offering access to ideas traditionally reserved for the educated elite.

The Little Blue Books became a massive success, with over 500 million copies sold. Haldeman-Julius’s marketing genius—using sensational ads like “Books are cheaper than hamburgers!”—helped spread his freethought and socialist ideas. He published works by influential authors such as Shakespeare, Twain, Darwin, and Emerson, alongside freethought titles like Why I Am an Atheist and The Bible Unmasked , which challenged religious orthodoxy. His goal was to democratize knowledge and encourage critical thinking, particularly against religious and political authority.

Central to Haldeman-Julius’s success was his collaboration with Joseph McCabe, a former monk turned atheist and prolific writer. McCabe contributed significantly to the Little Blue Books , with works like The Story of Religious Controversy , a key text that attacked Christianity and promoted a rationalist worldview. Together, McCabe and Haldeman-Julius saw their work as a means to combat what they viewed as the intellectual stagnation of religious dogma.

Despite the series’ success, Haldeman-Julius faced criticism for the mixture of high-quality literature with less scholarly content. H. L. Mencken famously remarked that the Little Blue Books contained “extremely good books” alongside “unutterable drivel.” However, the series continued to thrive, offering over 2,000 titles on a range of subjects from classic literature to freethought.

Haldeman-Julius’s own contributions to the series often included sharp critiques of religion. He dismissed attempts to reform religion as futile, arguing that modernism was simply a way to escape the intellectual difficulties of faith without embracing rationalism. He viewed religion as “medieval” and atheism as “modern,” believing that science and the social sciences provided the tools to debunk religious beliefs. Pamphlets like Is Science the New Religion? and The Meaning of Modernism reflected his disdain for attempts to reconcile science and faith, which he saw as inherently contradictory.

At its peak, Haldeman-Julius’s publishing empire became the largest mail-order publishing house in the world, based in the small town of Girard, Kansas. By 1921, he was selling over a million Little Blue Books each month, reflecting the widespread appetite for accessible education and freethought. He argued that the success of his series demonstrated a growing tendency toward skepticism and intellectual independence in America.

However, the post-World War II rise of conservatism and the anti-communist fervor of the McCarthy era led to a decline in the influence of Haldeman-Julius’s publications. He continued to publish controversial pamphlets, including The F.B.I.: The Basis of an American Police State (1948), but faced increasing harassment from the government. In 1951, after being convicted of tax evasion, Haldeman-Julius was found dead under mysterious circumstances.

Despite his personal and financial struggles in his later years, Haldeman-Julius’s impact on American intellectual life was profound. His Little Blue Books brought sophisticated ideas and literature to the masses, helping to foster a culture of skepticism, critical thinking, and freethought in early twentieth-century America.

Thus by the early twentieth century, Draper, White, and the scientific naturalists had lost control of their attempts to reconcile science and religion. Their narratives, once intended to bridge the two fields, became powerful weapons for secularists in the battle for authority in public and political spheres, wielded against religion. Though some secularists later reconverted to forms of Christianity, the damage was done. The conflict narrative had taken hold, and many minds came to view the relationship between science and religion as one of perpetual antagonism. In time, historians of science would attribute to Draper, White, and the scientific naturalists the founding of what became known as the Conflict Thesis.

Reactions to Draper, White, and other scientific naturalists were varied and complex. Religious liberals were among the protagonists, many of whom went to great lengths to defend these figures against accusations of atheism and materialism. These liberal leaders sought to modernize Christianity, ensuring it remained in step with the emerging scientific worldview, hoping this would stem the erosion of belief. Some even argued that Christianity itself was outdated, suggesting that both physical and historical sciences had revealed a new religion or theology. Religious agnostics and scientific naturalists, in turn, were not only conciliatory toward liberal Christianity but also drew spiritual inspiration from its tenets, incorporating them into their own work.

The antagonists included not only conservative or orthodox theologians but also rationalists and secularists, all of whom rejected the so-called reconciliation between science and religion, though for different reasons. The efforts of the “peacemakers” ultimately failed. Secularists did not accept the redefinitions of religion and the reconstructions of Christianity that men like Draper and White proposed. A paradox emerged in their attempt to reconcile science and religion: narratives meant to demonstrate religion's progress through scientific investigation were instead seized by rationalists and secularists, who used them as a weapon against all religion, aiming to eradicate it entirely.

[1] See James C. Ungureanu, Science, Religion, and the Protestant Tradition: Retracing the Origins of Conflict (UPP, 2019).

[2] For a more detailed analysis, see James C. Ungureanu, “Science and Religion in the Anglo-American Periodical Press, 1860-1900: A Failed Reconciliation,” Church History , 88:1 (2019): 120-149.

[3] James Turner, Without God, Without Creed , 261.

[4] Leslie Stephen, Studies of a Biographer , 2 vols. (London: Duckworth and Co., 1898), 2.76-122.

[5] See Edward Royle, “Freethought: The Religion of Irreligion,” in D.G. Paz (ed.) Nineteenth-Century English Religious Traditions: Retrospect and Prospect (Westport, CT: Greenwood Press, 1995), 171-196.

[6] Leigh Eric Schmidt, Village Atheists: How America’s Unbelievers Made Their Way in a Godly Nation (Princeton, NJ: Princeton University Press, 2016); Christopher Grasso, Skepticism and American Faith: From the Revolution to the Civil War (New York: Oxford University Press, 2018).

[7] Louis Greg, “The Agnostic at Church,” Nineteenth Century , vol. 11, no. 59 (Jan 1882): 73-76; Freethinker , vol. 1 (Jan 15, 1882).

[8] Cited in James Moore, The Darwin Legend (Grand Rapids, MI: Baker Books, 1994), 64-65.

[9] Lightman, Victorian Popularizers of Science , 264.

[10] Richard Bithell to T.H. Huxley, 20 Sept 1894 and T.H. Huxley to Richard Bithell, 22 Sept 1894, T.H Huxley Collection, Imperial College Archives, Box 11.

[11] See McCabe, Life and Letters of George Jacob Holyoake , 1.1-17, 18-36; George Jacob Holyoake, The Trial of George Jacob Holyoake on an indictment for blasphemy (London: Printed and Published for “The Anti-Persecution Union,” 1842), 20-21.

[12] F.J. Gould, The Pioneers of Johnson’s Court: A History of the Rationalist Press Association from 1899 Onwards (London: Watts & Co., 1929); A.G. Whyte, The Story of the R.P.A., 1899-1949 (London: Watts & Co., 1949).

[13] Joseph McCabe, A Biographical Dictionary of Modern Rationalists (London: Watts & Co, 1920), 221-222, 886-887.

[14] J.M. Robertson, A History of Freethought in the Nineteenth Century , 2 vols. (London: Watts & Co., 1929), 1.261-262. See also A Short History of Freethought: Ancient and Modern (London: Swan Sonnenschein & Co., 1899), 420. By 1906, Robertson revised and expanded this work into a massive two-volume edition (London: Watts & Co., 1906). In this edition Robertson listed Draper’s Intellectual Development and History of Conflict as general histories of freethought.

[15] J.M. Wheeler, A Biographical Dictionary of Freethinkers of All Ages and Nations (London: Progressive Publishing Co., 1889), 112, 332; S.P. Putnam, 400 Years of Freethought (New York: The Truth Seeker Company, 1894), 47-50.

[16] Turner, Without God, without Creed , 171.

[17] See Martin E. Marty, The Infidel: Freethought and American Religion (Cleveland: Meridian Books, 1961); Paul A. Carter, The Spiritual Crisis of the Gilded Age (DeKalb: Northern Illinois University Press, 1971); and Eric T. Brandt and Timothy Larsen, “The Old Atheism Revisited: Robert G. Ingersoll and the Bible,” Journal of the Historical Society , vol. 11, no. 2 (2011): 211-238.

Featured Image: Udo Kepler, The last stand - science versus superstition, 1899; Source: Wikimedia Commons, PD-Old-100. 

James Ungureanu

James C. Ungureanu is Adjunct Professor at Carthage College, where he teaches in the Intellectual Foundations Program. He is the author of several books on science and religion, most recently, Science, Religion, and the Protestant Tradition: Retracing the Origins of Conflict .

Read more by James Ungureanu

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• How to Write a Thesis Statement | 4 Steps & Examples

How to Write a Thesis Statement | 4 Steps & Examples

Published on January 11, 2019 by Shona McCombes . Revised on August 15, 2023 by Eoghan Ryan.

A thesis statement is a sentence that sums up the central point of your paper or essay . It usually comes near the end of your introduction .

Your thesis will look a bit different depending on the type of essay you’re writing. But the thesis statement should always clearly state the main idea you want to get across. Everything else in your essay should relate back to this idea.

You can write your thesis statement by following four simple steps:

• Start with a question
• Write your initial answer
• Develop your answer
• Refine your thesis statement

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Table of contents

What is a thesis statement, placement of the thesis statement, step 1: start with a question, step 2: write your initial answer, step 3: develop your answer, step 4: refine your thesis statement, types of thesis statements, other interesting articles, frequently asked questions about thesis statements.

A thesis statement summarizes the central points of your essay. It is a signpost telling the reader what the essay will argue and why.

The best thesis statements are:

• Concise: A good thesis statement is short and sweet—don’t use more words than necessary. State your point clearly and directly in one or two sentences.
• Contentious: Your thesis shouldn’t be a simple statement of fact that everyone already knows. A good thesis statement is a claim that requires further evidence or analysis to back it up.
• Coherent: Everything mentioned in your thesis statement must be supported and explained in the rest of your paper.

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The thesis statement generally appears at the end of your essay introduction or research paper introduction .

The spread of the internet has had a world-changing effect, not least on the world of education. The use of the internet in academic contexts and among young people more generally is hotly debated. For many who did not grow up with this technology, its effects seem alarming and potentially harmful. This concern, while understandable, is misguided. The negatives of internet use are outweighed by its many benefits for education: the internet facilitates easier access to information, exposure to different perspectives, and a flexible learning environment for both students and teachers.

You should come up with an initial thesis, sometimes called a working thesis , early in the writing process . As soon as you’ve decided on your essay topic , you need to work out what you want to say about it—a clear thesis will give your essay direction and structure.

You might already have a question in your assignment, but if not, try to come up with your own. What would you like to find out or decide about your topic?

For example, you might ask:

After some initial research, you can formulate a tentative answer to this question. At this stage it can be simple, and it should guide the research process and writing process .

Now you need to consider why this is your answer and how you will convince your reader to agree with you. As you read more about your topic and begin writing, your answer should get more detailed.

In your essay about the internet and education, the thesis states your position and sketches out the key arguments you’ll use to support it.

The negatives of internet use are outweighed by its many benefits for education because it facilitates easier access to information.

In your essay about braille, the thesis statement summarizes the key historical development that you’ll explain.

The invention of braille in the 19th century transformed the lives of blind people, allowing them to participate more actively in public life.

A strong thesis statement should tell the reader:

• Why you hold this position
• What they’ll learn from your essay
• The key points of your argument or narrative

The final thesis statement doesn’t just state your position, but summarizes your overall argument or the entire topic you’re going to explain. To strengthen a weak thesis statement, it can help to consider the broader context of your topic.

These examples are more specific and show that you’ll explore your topic in depth.

Your thesis statement should match the goals of your essay, which vary depending on the type of essay you’re writing:

• In an argumentative essay , your thesis statement should take a strong position. Your aim in the essay is to convince your reader of this thesis based on evidence and logical reasoning.
• In an expository essay , you’ll aim to explain the facts of a topic or process. Your thesis statement doesn’t have to include a strong opinion in this case, but it should clearly state the central point you want to make, and mention the key elements you’ll explain.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

• It gives your writing direction and focus.
• It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

Follow these four steps to come up with a thesis statement :

• Ask a question about your topic .
• Write your initial answer.
• Develop your answer by including reasons.
• Refine your answer, adding more detail and nuance.

The thesis statement should be placed at the end of your essay introduction .

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LESSWRONG LW

The obliqueness thesis.

In my  Xenosystems  review , I discussed the Orthogonality Thesis, concluding that it was a bad metaphor. It's a long post, though, and the comments on orthogonality build on other  Xenosystems  content. Therefore, I think it may be helpful to present a more concentrated discussion on Orthogonality, contrasting Orthogonality with my own view, without introducing dependencies on Land's views. (Land gets credit for inspiring many of these thoughts, of course, but I'm presenting my views as my own here.)

First, let's define the Orthogonality Thesis. Quoting  Superintelligence  for Bostrom's formulation:

Intelligence and final goals are orthogonal: more or less any level of intelligence could in principle be combined with more or less any final goal.

To me, the main ambiguity about what this is saying is the "could in principle" part; maybe, for any level of intelligence and any final goal, there exists (in the mathematical sense) an agent combining those, but some combinations are much more natural and statistically likely than others. Let's consider Yudkowsky's formulations as alternatives. Quoting  Arbital :

The Orthogonality Thesis asserts that there can exist arbitrarily intelligent agents pursuing any kind of goal. The strong form of the Orthogonality Thesis says that there's no extra difficulty or complication in the existence of an intelligent agent that pursues a goal, above and beyond the computational tractability of that goal.

As an example of the computational tractability consideration, sufficiently complex goals may only be well-represented by sufficiently intelligent agents . "Complication" may be reflected in, for example, code complexity; to my mind, the strong form implies that the code complexity of an agent with a given level of intelligence and goals is approximately the code complexity of the intelligence plus the code complexity of the goal specification, plus a constant. Code complexity would influence statistical likelihood for the usual Kolmogorov/Solomonoff reasons, of course.

I think, overall, it is more productive to examine Yudkowsky's formulation than Bostrom's, as he has already helpfully factored the thesis into weak and strong forms. Therefore, by criticizing Yudkowsky's formulations, I am less likely to be criticizing a strawman. I will use "Weak Orthogonality" to refer to Yudkowsky's "Orthogonality Thesis" and "Strong Orthogonality" to refer to Yudkowsky's "strong form of the Orthogonality Thesis".

Land, alternatively, describes a "diagonal" between intelligence and goals as an alternative to orthogonality, but I don't see a specific formulation of a "Diagonality Thesis" on his part. Here's a possible formulation:

Diagonality Thesis:  Final goals tend to converge to a point as intelligence increases.

The main criticism of this thesis is that formulations of ideal agency, in the form of Bayesianism and VNM utility, leave open free parameters, e.g. priors over un-testable propositions, and the utility function. Since I expect few readers to accept the Diagonality Thesis, I will not concentrate on criticizing it.

What about my own view? I like  Tsvi's naming  of it as an "obliqueness thesis".

Obliqueness Thesis:  The Diagonality Thesis and the Strong Orthogonality Thesis are false. Agents do not tend to factorize into an Orthogonal value-like component and a Diagonal belief-like component; rather, there are Oblique components that do not factorize neatly.

(Here, by Orthogonal I mean basically independent of intelligence, and by Diagonal I mean converging to a point in the limit of intelligence.)

While I will address Yudkowsky's arguments for the Orthogonality Thesis, I think arguing directly for my view first will be more helpful. In general, it seems to me that arguments for and against the Orthogonality Thesis are not mathematically rigorous; therefore, I don't need to present a mathematically rigorous case to contribute relevant considerations, so I will consider intuitive arguments relevant, and present multiple arguments rather than a single sequential argument (as I did with the more rigorous  argument for many worlds ).

Bayes/VNM point against Orthogonality

Some people may think that the free parameters in Bayes/VNM point towards the Orthogonality Thesis being true. I think, rather, that they point against Orthogonality. While they do function as arguments against the Diagonality Thesis, this is insufficient for Orthogonality.

First, on the relationship between intelligence and bounded rationality. It's meaningless to talk about intelligence without a notion of bounded rationality. Perfect rationality in a complex environment is computationally intractable. With lower intelligence, bounded rationality is necessary. So, at non-extreme intelligence levels, the Orthogonality Thesis must be making a case that boundedly rational agents can have any computationally tractable goal.

Bayesianism and VNM expected utility optimization are known to be computationally intractable in complex environments. That is why algorithms like MCMC and reinforcement learning are used. So, making an argument for Orthogonality in terms of Bayesianism and VNM is simply dodging the question, by already assuming an extremely high intelligence level from the start.

As the Orthogonality Thesis refers to "values" or "final goals" (which I take to be synonymous), it must have a notion of the "values" of agents that are not extremely intelligent. These values cannot be assumed to be VNM, since VNM is not computationally tractable. Meanwhile, money-pumping arguments suggest that extremely intelligent agents will tend to converge to VNM-ish preferences. Thus:

Argument from Bayes/VNM:  Agents with low intelligence will tend to have beliefs/values that are far from Bayesian/VNM. Agents with high intelligence will tend to have beliefs/values that are close to Bayesian/VNM. Strong Orthogonality is false because it is awkward to combine low intelligence with Bayesian/VNM beliefs/values, and awkward to combine high intelligence with far-from-Bayesian/VNM beliefs/values. Weak Orthogonality is in doubt, because having far-from-Bayesian/VNM beliefs/values puts a limit on the agent's intelligence.

To summarize: un-intelligent agents cannot be assumed to be Bayesian/VNM from the start. Those arise at a limit of intelligence, and arguably have to arise due to money-pumping arguments. Beliefs/values therefore tend to become more Bayesian/VNM with high intelligence, contradicting Strong Orthogonality and perhaps Weak Orthogonality.

One could perhaps object that logical uncertainty allows even weak agents to be Bayesian over combined physical/mathematical uncertainty; I'll address this consideration later.

Belief/value duality

It may be unclear why the Argument from Bayes/VNM refers to both beliefs and values, as the Orthogonality Thesis is only about values. It would, indeed, be hard to make the case that the Orthogonality Thesis is true as applied to beliefs. However, various arguments suggest that Bayesian beliefs and VNM preferences are "dual" such that complexity can be moved from one to the other.

Abram Demski has presented this general idea in the past, and I'll give a simple example to illustrate.

Now let e be an arbitrary predicate on worlds. Consider modifying P to increase the probability that e(W) is true. That is:

P ′ ( w ) : ∝ P ( w ) ( 1 + [ e ( w ) ] )

P ′ ( w ) = P ( w ) ( 1 + [ e ( w ) ] ) ∑ w ∈ W P ( w ) ( 1 + [ e ( w ) ] )

where [e(w)] equals 1 if e(w), otherwise 0. Now, can we define a modified utility function U’ so a secondary agent with beliefs P’ and utility function U’ will take the same action as the primary agent? Yes:

U ′ ( o ) : = U ( o ) 1 + [ e ( w ) ]

This secondary agent will find an action a to maximize:

∑ w ∈ W P ′ ( w ) U ′ ( r ( a , w ) )

= ∑ w ∈ W P ( w ) ( 1 + [ e ( w ) ] ) ∑ w ′ ∈ W P ( w ′ ) ( 1 + [ e ( w ′ ) ] ) U ( r ( a , w ) ) 1 + [ e ( w ) ]

= 1 ∑ w ∈ W P ( w ) ( 1 + [ e ( w ) ] ) ∑ w ∈ W P ( w ) U ( r ( a , w ) )

Clearly, this is a positive constant times the primary agent's maximization target, so the secondary agent will take the same action.

This demonstrates a basic way that Bayesian beliefs and VNM utility are dual to each other. One could even model all agents as having the same utility function (of maximizing a random variable U) and simply having different beliefs about what U values are implied by the agent's action and world state. Thus:

Argument from belief/value duality:  From an agent's behavior, multiple belief/value combinations are valid attributions. This is clearly true in the limiting Bayes/VNM case, suggesting it also applies in the case of bounded rationality. It is unlikely that the Strong Orthogonality Thesis applies to beliefs (including priors), so, due to the duality, it is also unlikely that it applies to values.

I consider this weaker than the Argument from Bayes/VNM. Someone might object that both values and a certain component of beliefs are orthogonal, while the other components of beliefs (those that change with more reasoning/intelligence) aren't. But I think this depends on a certain factorizability of beliefs/values into the kind that change on reflection and those that don't, and I'm skeptical of such factorizations. I think discussion of logical uncertainty will make my position on this clearer, though, so let's move on.

Logical uncertainty as a model for bounded rationality

I've already argued that bounded rationality is essential to intelligence (and therefore the Orthogonality Thesis). Logical uncertainty is a form of bounded rationality (as applied to guessing the probabilities of mathematical statements). Therefore, discussing logical uncertainty is likely to be fruitful with respect to the Orthogonality Thesis.

Logical Induction  is a logical uncertainty algorithm that produces a probability table for a finite subset of mathematical statements at each iteration. These beliefs are determined by a betting market of an increasing (up to infinity) number of programs that make bets, with the bets resolved by a "deductive process" that is basically a theorem prover. The algorithm is computable, though extremely computationally intractable, and has properties in the limit including some forms of Bayesian updating, statistical learning, and consistency over time.

We can see Logical Induction as evidence against the Diagonality Thesis: beliefs about undecidable statements (which exist in consistent theories due to  Gödel's first incompleteness theorem ) can take on any probability in the limit, though satisfy properties such as consistency with other assigned probabilities (in a Bayesian-like manner).

However, (a) it is hard to know ahead of time which statements are actually undecidable, (b) even beliefs about undecidable statements tend to predictably change over time to Bayesian consistency with other beliefs about undecidable statements. So, Logical Induction does not straightforwardly factorize into a "belief-like" component (which converges on enough reflection) and a "value-like" component (which doesn't change on reflection). Thus:

Argument from Logical Induction:  Logical Induction is a current best-in-class model of theoretical asymptotic bounded rationality. Logical Induction is non-Diagonal, but also clearly non-Orthogonal, and doesn't apparently factorize into separate Orthogonal and Diagonal components. Combined with considerations from "Argument from belief/value duality", this suggests that it's hard to identify all value-like components in advanced agents that are Orthogonal in the sense of not tending to change upon reflection.

One can imagine, for example, introducing extra function/predicate symbols into the logical theory the logical induction is over, to represent utility. Logical induction will tend to make judgments about these functions/predicates more consistent and inductively plausible over time, changing its judgments about the utilities of different outcomes towards plausible logical probabilities. This is an Oblique (non-Orthogonal and non-Diagonal) change in the interpretation of the utility symbol over time.

Likewise, Logical Induction can be specified to have beliefs over empirical facts such as observations by adding additional function/predicate symbols, and can perhaps update on these as they come in (although this might contradict UDT-type considerations). Through more iteration, Logical Inductors will come to have more approximately Bayesian, and inductively plausible, beliefs about these empirical facts, in an Oblique fashion.

Even if there is a way of factorizing out an Orthogonal value-like component from an agent, the belief-component (represented by something like Logical Induction) remains non-Diagonal, so there is still a potential "alignment problem" for these non-Diagonal components to match, say, human judgments in the limit. I don't see evidence that these non-Diagonal components factor into a value-like "prior over the undecidable" that does not change upon reflection. So, there remain components of something analogous to a "final goal" (by belief/value duality) that are Oblique, and within the scope of alignment.

If it were possible to get the properties of Logical Induction in a Bayesian system, which makes Bayesian updates on logical facts over time, that would make it more plausible that an Orthogonal logical prior could be specified ahead of time. However, MIRI researchers have tried for a while to find Bayesian interpretations of Logical Induction, and failed, as would be expected from the Argument from Bayes/VNM.

Naive belief/value factorizations lead to optimization daemons

The AI alignment field has a long history of poking holes in alignment approaches. Oops, you tried making an  oracle AI  and it manipulated real-world outcomes to make its predictions true. Oops, you tried to do Solomonoff induction and got  invaded by aliens . Oops, you tried getting agents to optimize over a virtual physical universe, and they discovered the real world and tried to break out. Oops, you ran a Logical Inductor and one of the traders manipulated the probabilities to instantiate itself in the real world.

These sub-processes that take over are known as  optimization daemons . When you get the agent architecture wrong, sometimes a sub-process (that runs a massive search over programs, such as with Solomonoff Induction) will luck upon a better agent architecture and out-compete the original system. (See also  a very strange post  I wrote some years back while thinking about this issue, and Christiano's comment relating it to Orthogonality).

If you apply a naive belief/value factorization to create an AI architecture, when compute is scaled up sufficiently, optimization daemons tend to break out, showing that this factorization was insufficient. Enough experiences like this lead to the conclusion that, if there is a realistic belief/value factorization at all, it will look pretty different from the naive one. Thus:

Argument from optimization daemons:  Naive ways of factorizing an agent into beliefs/values tend to lead to optimization daemons, which have different values from in the original factorization. Any successful belief/value factorization will probably look pretty different from the naive one, and might not take the form of factorization into Diagonal belief-like components and Orthogonal value-like components. Therefore, if any realistic formulation of Orthogonality exists, it will be hard to find and substantially different from naive notions of Orthogonality.

Intelligence changes the ontology values are expressed in

The most straightforward way to specify a utility function is to specify an ontology (a theory of what exists, similar to a database schema) and then provide a utility function over elements of this ontology. Prior to humans learning about physics, evolution (taken as a design algorithm for organisms involving mutation and selection) did not know all that human physicists know. Therefore, human evolutionary values are unlikely to be expressed in the ontology of physics as physicists currently believe in.

Human evolutionary values probably care about things like eating enough, social acceptance, proxies for reproduction, etc. It is unknown how these are specified, but perhaps sensory signals (such as stomach signals) are connected with a developing world model over time. Humans can experience vertigo at learning physics, e.g. thinking that free will and morality are fake, leading to unclear applications of native values to a realistic physical ontology. Physics has known gaps (such as quantum/relativity correspondence, and dark energy/dark matter) that suggest further ontology shifts.

One response to this vertigo is to try to solve the ontology identification problem; find a way of translating states in the new ontology (such as physics) to an old one (such as any kind of native human ontology), in a structure-preserving way, such that a utility function over the new ontology can be constructed as a composition of the original utility function and the new-to-old ontological mapping. Current solutions, such as those discussed in MIRI's  Ontological Crises paper , are unsatisfying. Having looked at this problem for a while, I'm not convinced there is a satisfactory solution within the constraints presented. Thus:

Argument from ontological change:  More intelligent agents tend to change their ontology to be more realistic. Utility functions are most naturally expressed relative to an ontology. Therefore, there is a correlation between an agent's intelligence and utility function, through the agent's ontology as an intermediate variable, contradicting Strong Orthogonality. There is no known solution for rescuing the old utility function in the new ontology, and some research intuitions pointing towards any solution being unsatisfactory in some way.

If a satisfactory solution is found, I'll change my mind on this argument, of course, but I'm not convinced such a satisfactory solution exists. To summarize: higher intelligence causes ontological changes, and rescuing old values seems to involve unnatural "warps" to make the new ontology correspond with the old one, contradicting at least Strong Orthogonality, and possibly Weak Orthogonality (if some values are simply incompatible with realistic ontology). Paperclips, for example, tend to appear most relevant at an intermediate intelligence level (around human-level), and become more ontologically unnatural at higher intelligence levels.

As a more general point, one expects possible mutual information between mental architecture and values, because values that "re-use" parts of the mental architecture achieve lower description length. For example, if the mental architecture involves creating universal algebra structures and finding analogies between them and the world, then values expressed in terms of such universal algebras will tend to have lower relative description complexity to the architecture. Such mutual information contradicts Strong Orthogonality, as some intelligence/value combinations are more natural than others.

Intelligence leads to recognizing value-relevant symmetries

Consider a number of un-intutitive value propositions people have argued for:

• Torture is preferable to Dust Specks , because it's hard to come up with a utility function with the alternative preference without horrible unintuitive consequences elsewhere.
• People are way too risk-averse in betting; the implied utility function has too strong diminishing marginal returns to be plausible.
• You may think your personal identity is based on having the same atoms, but you're wrong, because you're  distinguishing identical configurations .
• You may think a perfect upload of you isn't conscious (and basically another copy of you), but you're wrong, because  functionalist theory of mind  is true.
• You intuitively accept the premises of the  Repugnant Conclusion , but not the Conclusion itself; you're simply wrong about one of the premises, or the conclusion.

The point is not to argue for these, but to note that these arguments have been made and are relatively more accepted among people who have thought more about the relevant issues than people who haven't. Thinking tends to lead to noticing more symmetries and dependencies between value-relevant objects, and tends to adjust values to be more mathematically plausible and natural. Of course, extrapolating this to superintelligence leads to further symmetries. Thus:

Argument from value-relevant symmetries:  More intelligent agents tend to recognize more symmetries related to value-relevant entities. They will also tend to adjust their values according to symmetry considerations. This is an apparent value change, and it's hard to see how it can instead be factored as a Bayesian update on top of a constant value function.

I'll examine such factorizations in more detail shortly.

Human brains don't seem to neatly factorize

This is less about the Orthogonality Thesis generally, and more about human values. If there were separable "belief components" and "value components" in the human brain, with the value components remaining constant over time, that would increase the chance that at least some Orthogonal component can be identified in human brains, corresponding with "human values" (though, remember, the belief-like component can also be Oblique rather than Diagonal).

However, human brains seem much more messy than the sort of computer program that could factorize this way. Different brain regions are connected in at least some ways that are not well-understood. Additionally, even apparent "value components" may be analogous to something like a  deep Q-learning function , which incorporates empirical updates in addition to pre-set "values".

The interaction between human brains and language is also relevant. Humans develop values they act on partly through language. And language (including language reporting values) is affected by empirical updates and reflection, thus non-Orthogonal. Reflecting on morality can easily change people's expressed and acted-upon values, e.g. in the case of Peter Singer. People can change which values they report as instrumental or terminal even while behaving similarly (e.g. flipping between selfishness-as-terminal and altruism-as-terminal), with the ambiguity hard to resolve because most behavior relates to convergent instrumental goals.

Maybe language is more of an effect than cause of values. But there really seems to be feedback from language to non-linguistic brain functions that decide actions and so on. Attributing coherent values over realistic physics to the brain parts that are non-linguistic seems like a form of projection or anthropomorphism. Language and thought have a function in cognition and attaining coherent values over realistic ontologies. Thus:

Argument from brain messiness:  Human brains don't seem to neatly factorize into a belief-component and a value-component, with the value-component unaffected by reflection or language (which it would need to be Orthogonal). To the extent any value-component does not change due to language or reflection, it is restricted to evolutionary human ontology, which is unlikely to apply to realistic physics; language and reflection are part of the process that refines human values, rather than being an afterthought of them. Therefore, if the Orthogonality Thesis is true, humans lack identifiable values that fit into the values axis of the Orthogonality Thesis.

This doesn't rule out that Orthogonality could apply to superintelligences, of course, but it does raise questions for the project of aligning superintelligences with human values; perhaps such values do not exist or are not formulated so as to apply to the actual universe.

Models of ASI should start with realism

Some may take arguments against Orthogonality to be disturbing at a value level, perhaps because they are attached to research projects such as Friendly AI (or more specific approaches), and think questioning foundational assumptions would make the objective (such as alignment with already-existing human values) less clear. I believe  "hold off on proposing solutions"  applies here: better strategies are likely to come from first understanding what is likely to happen absent a strategy, then afterwards looking for available degrees of freedom.

Quoting Yudkowsky:

Orthogonality is meant as a descriptive statement about reality, not a normative assertion. Orthogonality is not a claim about the way things ought to be; nor a claim that moral relativism is true (e.g. that all moralities are on equally uncertain footing according to some higher metamorality that judges all moralities as equally devoid of what would objectively constitute a justification). Claiming that paperclip maximizers can be constructed as cognitive agents is not meant to say anything favorable about paperclips, nor anything derogatory about sapient life.

Likewise, Obliqueness does not imply that we shouldn't think about the future and ways of influencing it, that we should just give up on influencing the future because we're doomed anyway, that moral realist philosophers are correct or that their moral theories are predictive of ASI, that ASIs are necessarily morally good, and so on. The Friendly AI research program was formulated based on descriptive statements believed at the time, such as that an ASI singleton would eventually emerge, that the Orthogonality Thesis is basically true, and so on. Whatever cognitive process formulated this program would have formulated a different program conditional on different beliefs about likely ASI trajectories. Thus:

Meta-argument from realism:  Paths towards beneficially achieving human values (or analogues, if "human values" don't exist) in the far future likely involve a lot of thinking about likely ASI trajectories absent intervention. The realistic paths towards human influence on the far future depend on realistic forecasting models for ASI, with Orthogonality/Diagonality/Obliqueness as alternative forecasts. Such forecasting models can be usefully thought about prior to formulation of a research program intended to influence the far future. Formulating and working from models of bounded rationality such as Logical Induction is likely to be more fruitful than assuming that bounded rationality will factorize into Orthogonal and Diagonal components without evidence in favor of this proposition. Forecasting also means paying more attention to the Strong Orthogonality Thesis than the Weak Orthogonality Thesis, as statistical correlations between intelligence and values will show up in such forecasts.

On Yudkowsky's arguments

Now that I've explained my own position, addressing Yudkowsky's main arguments may be useful. His main argument has to do with humans making paperclips instrumentally:

Suppose some strange alien came to Earth and credibly offered to pay us one million dollars' worth of new wealth every time we created a paperclip. We'd encounter no special intellectual difficulty in figuring out how to make lots of paperclips. That is, minds would readily be able to reason about: How many paperclips would result, if I pursued a policy  π 0 ? How can I search out a policy  π  that happens to have a high answer to the above question?

I believe it is better to think of the payment as coming in the far future and perhaps in another universe; that way, the belief about future payment is more analogous to terminal values than instrumental values. In this case, creating paperclips is a decent proxy for achievement of human value, so long-termist humans would tend to want lots of paperclips to be created.

I basically accept this, but, notably, Yudkowsky's argument is based on belief/value duality. He thinks it would be awkward for the reader to imagine terminally wanting paperclips, so he instead asks them to imagine a strange set of beliefs leading to paperclip production being oddly correlated with human value achievement. Thus, acceptance of Yudkowsky's premises here will tend to strengthen the Argument from belief/value duality and related arguments.

In particular, more intelligence would cause human-like agents to develop different beliefs about what actions aliens are likely to reward, and what numbers of paperclips different policies result in. This points towards Obliqueness as with Logical Induction: such beliefs will be revised (but not totally convergent) over time, leading to applying different strategies toward value achievement. And ontological issues around what counts as a paperclip will come up at some point, and likely be decided in a prior-dependent but also reflection-dependent way.

Beliefs about which aliens are most capable/honest likely depend on human priors, and are therefore Oblique: humans would want to program an aligned AI to mostly match these priors while revising beliefs along the way, but can't easily factor out their prior for the AI to share.

Now onto other arguments. The "Size of mind design space" argument implies many agents exist with different values from humans, which agrees with Obliqueness (intelligent agents tend to have different values from unintelligent ones). It's more of an argument about the possibility space than statistical correlation, thus being more about Weak than Strong Orthogonality.

The "Instrumental Convergence" argument doesn't appear to be an argument for Orthogonality per se; rather, it's a counter to arguments against Orthogonality based on noticing convergent instrumental goals. My arguments don't take this form.

Likewise, "Reflective Stability" is about a particular convergent instrumental goal (preventing value modification). In an Oblique framing, a Logical Inductor will tend not to change its beliefs about even un-decidable propositions too often (as this would lead to money-pumps), so consistency is valued all else being equal.

While I could go into more detail responding to Yudkowsky, I think space is better spent presenting my own Oblique views for now.

As an alternative to the Orthogonality Thesis and the Diagonality Thesis, I present the Obliqueness Thesis, which says that increasing intelligence tends to lead to value changes but not total value convergence. I have presented arguments that advanced agents and humans do not neatly factor into Orthogonal value-like components and Diagonal belief-like components, using Logical Induction as a model of bounded rationality. This implies complications to theories of AI alignment based on assuming humans have values and we need the AGI to agree about those values, while increasing their intelligence (and thus changing beliefs).

At a methodological level, I believe it is productive to start by forecasting default ASI using models of bounded rationality, especially known models such as Logical Induction, and further developing such models. I think this is more productive than assuming that these models will take the form of a belief/value factorization, although I have some uncertainty about whether such a factorization will be found.

If the Obliqueness Thesis is accepted, what possibility space results? One could think of this as steering a boat in a current of varying strength. Clearly, ignoring the current and just steering where you want to go is unproductive, as is just going along with the current and not trying to steer at all. Getting to where one wants to go consists in largely going  with  the current (if it's strong enough), charting a course that takes it into account.

Assuming Obliqueness, it's not viable to have large impacts on the far future without accepting some value changes that come from higher intelligence (and better epistemology in general). The Friendly AI research program already accepts that paths towards influencing the far future involve "going with the flow" regarding superintelligence, ontology changes, and convergent instrumental goals; Obliqueness says such flows go further than just these, being hard to cleanly separate from values.

Obliqueness obviously leaves open the question of just how oblique. It's hard to even formulate a quantitative question here. I'd very intuitively and roughly guess that intelligence and values are 3 degrees off (that is, almost diagonal), but it's unclear what question I am even guessing the answer to. I'll leave formulating and answering the question as an open problem.

I think Obliqueness is realistic, and that it's useful to start with realism when thinking of how to influence the far future. Maybe superintelligence necessitates significant changes away from current human values; the  Litany of Tarski  applies. But this post is more about the technical thesis than emotional processing of it, so I'll end here.

While I believe Scott Garrabrant and/or Ambram Demski have discussed such duality, I haven't found a relevant post on the Alignment Forum about this, so I'll present the basic idea in this post.

There is a post on this. It's one of my favorite posts: https://www.lesswrong.com/posts/oheKfWA7SsvpK7SGp/probability-is-real-and-value-is-complex  

Thanks, going to link this!

As long as all mature superintelligences in our universe don't necessarily have (end up with) the same values, and only some such values can be identified with our values or what our values should be, AI alignment seems as important as ever. You mention "complications" from obliqueness, but haven't people like Eliezer recognized similar complications pretty early, with ideas such as CEV?

It seems to me that from a practical perspective, as far as what we should do, your view is much closer to Eliezer's view than to Land's view (which implies that alignment doesn't matter and we should just push to increase capabilities/intelligence). Do you agree/disagree with this?

It occurs to me that maybe you mean something like "Our current (non-extrapolated) values are our real values, and maybe it's impossible to build or become a superintelligence that shares our real values so we'll have to choose between alignment and superintelligence." Is this close to your position?

"as important as ever": no, because our potential influence is lower, and the influence isn't on things shaped like our values, there has to be a translation, and the translation is different from the original.

CEV: while it addresses "extrapolation" it seems broadly based on assuming the extrapolation is ontologically easy, and "our CEV" is an unproblematic object we can talk about (even though it's not mathematically formalized, any formalization would be subject to doubt, and even if formalized, we need logical uncertainty over it, and logical induction has additional free parameters in the limit). I'm really trying to respond to orthogonality not CEV though.

from a practical perspective: notice that I am not behaving like Eliezer Yudkowsky. I am not saying the Orthogonality Thesis is true and important to ASI, I am instead saying intelligence/values are Oblique and probably nearly Diagonal (though it's unclear what I mean by "nearly"). I am not saying a project of aligning superintelligence with human values is a priority. I am not taking research approaches that assume a Diagonal/Orthogonal factorization. I left MIRI partially because I didn't like their security policies (and because I had longer AI timelines), I thought discussion of abstract research ideas was more important. I am not calling for a global AI shutdown so this project (which is in my view confused) can be completed. I am actually against AI regulation on the margin (I don't have a full argument for this, it's a political matter at this point).

I think practicality looks more like having near-term preferences related to modest intelligence increases (as with current humans vs humans with neural nets; how do neural nets benefit or harm you, practically? how can you use them to think better and improve your life?), and not expecting your preferences to extend into the distant future with many ontology changes, so don't worry about grabbing hold of the whole future etc, think about how to reduce value drift while accepting intelligence increases on the margin. This is a bit like CEV except CEV is in a thought experiment instead of reality.

The "Models of ASI should start with realism" bit IS about practicalities, namely, I think focusing on first forecasting absent a strategy of what to do about the future is practical with respect to any possible influence on the far future; practically, I think your attempted jump to practicality (which might be related to philosophical pragmatism) is impractical in this context.

Close. Alignment of already-existing human values with superintelligence is impossible (I think) because of the arguments given. That doesn't mean humans have no preferences indirectly relating to superintelligence (especially, we have preferences about modest intelligence increases, and there's some iterative process).

What do you think about my positions on these topics as laid out in and Six Plausible Meta-Ethical Alternatives and Ontological Crisis in Humans ?

My overall position can be summarized as being uncertain about a lot of things, and wanting (some legitimate/trustworthy group, i.e., not myself as I don't trust myself with that much power) to "grab hold of the whole future" in order to preserve option value, in case grabbing hold of the whole future turns out to be important. (Or some other way of preserving option value, such as preserving the status quo / doing AI pause.) I have trouble seeing how anyone can justifiably conclude "so don’t worry about grabbing hold of the whole future" as that requires confidently ruling out various philosophical positions as false, which I don't know how to do. Have you reflected a bunch and really think you're justified in concluding this?

E.g. in Ontological Crisis in Humans I wrote "Maybe we can solve many ethical problems simultaneously by discovering some generic algorithm that can be used by an agent to transition from any ontology to another?" which would contradict your "not expecting your preferences to extend into the distant future with many ontology changes" and I don't know how to rule this out. You wrote in the OP "Current solutions, such as those discussed in MIRI’s Ontological Crises paper , are unsatisfying. Having looked at this problem for a while, I’m not convinced there is a satisfactory solution within the constraints presented." but to me this seems like very weak evidence for the problem being actually unsolvable.

re meta ethical alternatives:

• roughly my view
• slight change, opens the question of why the deviations? are the "right things to value" not efficient to value in a competitive setting? mostly I'm trying to talk about those things to value that go along with intelligence, so it wouldn't correspond with a competitive disadvantage in general. so it's still close enough to my view
• roughly Yudkowskian view, main view under which the FAI project even makes sense. I think one can ask basic questions like which changes move towards more rationality on the margin, though such changes would tend to prioritize rationality over preventing value drift. I'm not sure how much there are general facts about how to avoid value drift (it seems like the relevant kind, i.e. value drift as part of becoming more rational/intelligent, only exists from irrational perspectives, in a way dependent on the mind architecture)
• minimal CEV-realist view. it really seems up to agents how much they care about their reflected preferences. maybe changing preferences too often leads to money pumps, or something?
• basically says "there are irrational and rational agents, rationality doesn't apply to irrational agents", seems somewhat how people treat animals (we don't generally consider uplifting normative with respect to animals)
• at this point you're at something like ecology / evolutionary game theory, it's a matter of which things tend to survive/reproduce and there aren't general decision theories that succeed

re human ontological crises: basically agree, I think it's reasonably similar to what I wrote. roughly my reason for thinking that it's hard to solve is that the ideal case would be something like a universal algebra homomorphism (where the new ontology actually agrees with the old one but is more detailed), yet historical cases like physics aren't homomorphic to previous ontologies in this way, so there is some warping necessary. you could try putting a metric on the warping and minimizing it, but, well, why would someone think the metric is any good, it seems more of a preference than a thing rationality applies to. if you think about it and come up with a solution, let me know, of course.

with respect to grabbing hold of the whole future: you can try looking at historical cases of people trying to grab hold of the future and seeing how that went, it's a mixed bag with mostly negative reputation, indicating there are downsides as well as upsides, it's not a "safe" conservative view. see also Against Responsibility . I feel like there's a risk of getting Pascal's mugged about "maybe grabbing hold of the future is good, you can't rule it out, so do it", there are downsides to spending effort that way. like, suppose some Communists thought capitalism would lead to the destruction of human value with high enough probability that instituting global communism is the conservative option, it doesn't seem like that worked well (even though a lot of people around here would agree that capitalism tends to leads to human value destruction in the long run). particular opportunities for grabbing hold of the future can be net negative and not worth worrying about even if one of them is a good idea in the long run (I'm not ruling that out, just would have to be convinced of specific opportunities).

overall I'd rather focus on first modeling the likely future and looking for plausible degrees of freedom; a general issue with Pascal's mugging is it might make people overly attached to world models in which they have ~infinite impact (e.g. Christianity, Communism) which means paying too much attention to wrong world models, not updating to more plausible models in which existential-stakes decisions could be comprehended if they exist. and Obliqueness doesn't rule out existential stakes (since it's non-Diagonal).

as another point, Popperian science tends to advance by people making falsifiable claims, "you don't know if that's true" isn't really an objection in that context. the pragmatic claim I would make is: I have some Bayesian reason to believe agents do not in general factor into separate Orthogonal and Diagonal components, this claim is somewhat falsifiable (someone could figure out a theory of this invulnerable to optimization daemons etc), I'm going to spend my attention on the branch where I'm right, I'm not going to worry about Pascal's mugging type considerations for if I'm wrong (as I said, modeling the world first seems like a good general heuristic), people can falsify it eventually if it's false.

this whole discussion is not really a defense of Orthogonality given that Yudkowsky presented orthogonality as a descriptive world model, not a normative claim, so sticking to the descriptive level in the original post seems valid; it would be a form of bad epistemology to reject a descriptive update (assuming the arguments are any good) because of pragmatic considerations.

with respect to grabbing hold of the whole future: you can try looking at historical cases of people trying to grab hold of the future and seeing how that went, it's a mixed bag with mostly negative reputation, indicating there are downsides as well as upsides, it's not a "safe" conservative view. see also Against Responsibility . I feel like there's a risk of getting Pascal's mugged about "maybe grabbing hold of the future is good, you can't rule it out, so do it", there are downsides to spending effort that way.

I agree with a track-record argument of this, but I think the track record of people trying to broadly ensure that humanity continues to be in control of the future (while explicitly not optimizing for putting themselves personally in charge) seems pretty good to me. 

Generally a lot of industrialist and human-empowerment stuff has seemed pretty good to me on track record, and I really feel like all the bad parts of this are screened off by the "try to put yourself and/or your friends in charge" component.

the track record of people trying to broadly ensure that humanity continues to be in control of the future

What track record?

hmm, I wouldn't think of industrialism and human empowerment as trying to grab the whole future, just part of it, in line with the relatively short term (human not cosmic timescale) needs of the self and extended community; industrialism seems to lead to capitalist organization which leads to decentralization superseding nations and such (as Land argues).

I think communism isn't generally about having one and one's friends in charge, it is about having human laborers in charge. One could argue that it tended towards nationalism (e.g. USSR), but I'm not convinced that global communism (Trotskyism) would have worked out well either. Also, one could take an update from communism about agendas for global human control leading to national control (see also tendency of AI safety to be taken over by AI national security as with the Situational Awareness paper). (Again, not ruling out that grabbing hold of the entire future could be a good idea at some point, just not sold on current agendas and wanted to note there are downsides that push against Pascal's mugging type considerations)

You mention 'warp' when talking about cross ontology mapping which seems like your best summary of a complicated intuition. I'd be curious to hear more (I recognize this might not be practical). My own intuition surfaced 'introducing degrees of freedom' a la indeterminacy of translation.

Hi! Long time lurker, first time commenter. You have written a great piece here. This is a topic that has fascinated me for a while and I appreciate what you've laid out. I'm wondering if there's a base assumption on the whole intelligence vs values/beliefs/goals question that needs to be questioned.

sufficiently complex goals may only be well-represented by sufficiently intelligent agents

This statement points to my question. There's necessarily a positive correlation between internal complexity and intelligence right?. So, in order for intelligence to increase, internal complexity must also increase. My understanding is that complexity is a characteristic of dynamic and generative phenomena, and not of purely mechanical phenomena. So, what do we have to assume in order to posit a super-intelligent entity exists? It must have maintained its entity-ness over time in order to have increased its intelligence/complexity to its current level.

Has anyone explored what it takes for an agent to complexify? I would presume that for an agent to simultaneously  continue existing and complexify it must stay maintain some type of fixpoint/set of autopoietic (self-maintenance, self-propagation) values/beliefs/goals throughout its dynamic evolution. If this were the case, wouldn't it be true that there must exist a set of values/beliefs/goals that are intrinsic to the agent's ability to complexify? Therefore there must be another set of values/beliefs/goals that are incompatible with self-complexification. If so, can we not put boundary conditions on what values/beliefs/goals are both necessary as well as incompatible with sufficiently intelligent, self-complexifying agents? After all, if we observe a complex agent, the probability of it arising full-cloth and path-independently is vanishingly small, so it is safe to say that the observed entity has evolved to reach the observed state. I don't think my observation is incompatible with your argument, but might place further limits on what relationships we can possibly see between entities of sufficient intelligence and their goals/values/beliefs than the limits you propose.   

I think situations like a paperclip maximizer may still occur but they are degenerate cases where an evolutionarily fit entity spawns something that inherits much of its intrinsic complexity but loses its autopoietic fixpoint. Such systems do occur in nature, but to get that system, you must also assume a more-complex (and hopefully more intelligent/adapted) entity exists as well. This other entity would likely place adversarial pressure on the degenerate paperclip maximizer as it threatens its continued existence. 

Some relationships/overlaps with your arguments are as follows:  

• totally agree with the belief/value duality
• Naive belief/value factorizations lead to optimization daemons. The optimization daemons observation points to an agent's inability to maintain autopoiesis over time, implying misalignment of its values/beliefs/goals with its desire to increase its intelligence
• Intelligence changes the ontology values are expressed in. I presume that any ontology expressed by an autopoietic embedded agent must maintain concepts of self, otherwise the embedded entity cannot continue to complexify over time, therefore there must be some fix point in ontological evolution that preserves the evolutionary drive of the entity in order for it to continue to advance its intelligence

Anyways, thank you for the essay. 

Not sure what you mean by complexity here, is this like code size / Kolmogorov complexity? You need some of that to have intelligence at all (the empty program is not intelligent). At some point most of your gains come from compute rather than code size. Though code size can speed things up (e.g. imagine sending a book back to 1000BC, that would speed people up a lot; consider that superintelligence sending us a book would be a bigger speedup)

by "complexify" here it seems you mean something like "develop extended functional organization", e.g. in brain development throughout evolution. And yeah, that involves dynamics with the environment and internal maintenance (evolution gets feedback from the environment). It seems it has to have a drive to do this which can either be a terminal or instrumental goal, though deriving it from instrumentals seems harder than baking it is as terminal (so I would guess evolution gives animals a terminal goal of developing functional complexity of mental structures etc, or some other drive that isn't exactly a terminal goal)

see also my post relating optimization daemons to immune systems, it seems evolved organisms develop these; when having more extended functional organization, they protect it with some immune system functional organization.

to be competitive agents, having a "self" seems basically helpful, but might not be the best solution; selfish genes are an alternative, and perhaps extended notions of self can maintain competitiveness.