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About this unit.
Significance tests give us a formal process for using sample data to evaluate the likelihood of some claim about a population value. Learn how to conduct significance tests and calculate p-values to see how likely a sample result is to occur by random chance. You'll also see how we use p-values to make conclusions about hypotheses.
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Published on January 28, 2020 by Rebecca Bevans . Revised on June 22, 2023.
Statistical tests are used in hypothesis testing . They can be used to:
Statistical tests assume a null hypothesis of no relationship or no difference between groups. Then they determine whether the observed data fall outside of the range of values predicted by the null hypothesis.
If you already know what types of variables you’re dealing with, you can use the flowchart to choose the right statistical test for your data.
Statistical tests flowchart
What does a statistical test do, when to perform a statistical test, choosing a parametric test: regression, comparison, or correlation, choosing a nonparametric test, flowchart: choosing a statistical test, other interesting articles, frequently asked questions about statistical tests.
Statistical tests work by calculating a test statistic – a number that describes how much the relationship between variables in your test differs from the null hypothesis of no relationship.
It then calculates a p value (probability value). The p -value estimates how likely it is that you would see the difference described by the test statistic if the null hypothesis of no relationship were true.
If the value of the test statistic is more extreme than the statistic calculated from the null hypothesis, then you can infer a statistically significant relationship between the predictor and outcome variables.
If the value of the test statistic is less extreme than the one calculated from the null hypothesis, then you can infer no statistically significant relationship between the predictor and outcome variables.
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You can perform statistical tests on data that have been collected in a statistically valid manner – either through an experiment , or through observations made using probability sampling methods .
For a statistical test to be valid , your sample size needs to be large enough to approximate the true distribution of the population being studied.
To determine which statistical test to use, you need to know:
Statistical tests make some common assumptions about the data they are testing:
If your data do not meet the assumptions of normality or homogeneity of variance, you may be able to perform a nonparametric statistical test , which allows you to make comparisons without any assumptions about the data distribution.
If your data do not meet the assumption of independence of observations, you may be able to use a test that accounts for structure in your data (repeated-measures tests or tests that include blocking variables).
The types of variables you have usually determine what type of statistical test you can use.
Quantitative variables represent amounts of things (e.g. the number of trees in a forest). Types of quantitative variables include:
Categorical variables represent groupings of things (e.g. the different tree species in a forest). Types of categorical variables include:
Choose the test that fits the types of predictor and outcome variables you have collected (if you are doing an experiment , these are the independent and dependent variables ). Consult the tables below to see which test best matches your variables.
Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests.
The most common types of parametric test include regression tests, comparison tests, and correlation tests.
Regression tests look for cause-and-effect relationships . They can be used to estimate the effect of one or more continuous variables on another variable.
Predictor variable | Outcome variable | Research question example | |
---|---|---|---|
What is the effect of income on longevity? | |||
What is the effect of income and minutes of exercise per day on longevity? | |||
Logistic regression | What is the effect of drug dosage on the survival of a test subject? |
Comparison tests look for differences among group means . They can be used to test the effect of a categorical variable on the mean value of some other characteristic.
T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults).
Predictor variable | Outcome variable | Research question example | |
---|---|---|---|
Paired t-test | What is the effect of two different test prep programs on the average exam scores for students from the same class? | ||
Independent t-test | What is the difference in average exam scores for students from two different schools? | ||
ANOVA | What is the difference in average pain levels among post-surgical patients given three different painkillers? | ||
MANOVA | What is the effect of flower species on petal length, petal width, and stem length? |
Correlation tests check whether variables are related without hypothesizing a cause-and-effect relationship.
These can be used to test whether two variables you want to use in (for example) a multiple regression test are autocorrelated.
Variables | Research question example | |
---|---|---|
Pearson’s | How are latitude and temperature related? |
Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.
Predictor variable | Outcome variable | Use in place of… | |
---|---|---|---|
Spearman’s | |||
Pearson’s | |||
Sign test | One-sample -test | ||
Kruskal–Wallis | ANOVA | ||
ANOSIM | MANOVA | ||
Wilcoxon Rank-Sum test | Independent t-test | ||
Wilcoxon Signed-rank test | Paired t-test | ||
This flowchart helps you choose among parametric tests. For nonparametric alternatives, check the table above.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Methodology
Research bias
Statistical tests commonly assume that:
If your data does not meet these assumptions you might still be able to use a nonparametric statistical test , which have fewer requirements but also make weaker inferences.
A test statistic is a number calculated by a statistical test . It describes how far your observed data is from the null hypothesis of no relationship between variables or no difference among sample groups.
The test statistic tells you how different two or more groups are from the overall population mean , or how different a linear slope is from the slope predicted by a null hypothesis . Different test statistics are used in different statistical tests.
Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test . Significance is usually denoted by a p -value , or probability value.
Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis .
When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant.
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .
Discrete and continuous variables are two types of quantitative variables :
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Bevans, R. (2023, June 22). Choosing the Right Statistical Test | Types & Examples. Scribbr. Retrieved August 2, 2024, from https://www.scribbr.com/statistics/statistical-tests/
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Hypothesis testing refers to the systematic and scientific method of examining whether the hypothesis set by the researcher is valid or not. Hypothesis testing verifies that the findings of an experiment are valid and the particular results did not happen by chance. If the particular results have occurred by chance then that experiment can not be repeated and its findings won’t be reliable. For example, if you conduct a study that finds that a particular drug is responsible for the blood pressure problem in diabetic patients. But, when you repeat this experiment and it does not give the same results, no one would trust this experiment’s findings. Hence, hypothesis testing is a very crucial step to verify the experimental findings. The main criterion of hypothesis testing is to check whether the null hypothesis is rejected or retained. The null hypothesis assumes that there does not exist any relationship between the variables under investigation, while the alternate hypothesis confirms the association between the variables under investigation. If the null hypothesis is rejected, it means that alternative hypotheses (research hypothesis) are accepted, and if the null hypothesis is accepted, the alternate hypothesis is rejected automatically. In this article, we’ll learn about hypothesis testing and various real-life examples of hypothesis testing.
The hypothesis testing broadly involves the following steps,
Let us understand these steps through the following example,
Suppose the researcher wants to examine whether the memorizing power of students improves after consuming caffeine or not. To examine this he conducts experiments, the experiment involves two groups say group A (experimental group) and group B (control group). Group A consumed the coffee before the memory test, while group B consumed the water before the memory test. The average normally distributed score of the people of the experimental group has a standard deviation of 4 and a mean of 19. On the basis of the score, the researcher can state that there is an association between the two variables, i.e., the memory power and the caffeine, but the researcher can not predict any particular direction, i.e., which out of the experimental group and the control group had performed better in the memory tests. Hence, the level of significance value, i.e., 5 per cent will help to draw the conclusion. Following is the stepwise hypothesis testing of this example,
Step 1: Formulating Null hypothesis and alternate hypothesis
There exist two sample populations, i.e., group A and group B.
Group A: People who consumed coffee before the experiment
Group B: People who consumed water before the experiment.
On the basis of this, the null hypothesis and the alternative hypothesis would be as follows.
Alternate Hypothesis: Group A will perform differently from Group B, i.e., there exists an association between the two variables.
Null Hypothesis: There will not be any difference between the performance of both groups, i.e., Group A and Group B both will perform similarly.
Step 2: Characteristics of the comparison distribution
The characteristics of the comparison distribution in this example are given below,
Population Mean = 19
Standard Deviation= 4, normally distributed.
Step 3: Cut off score
In this test the direction of effect is not stated, i.e., it is a two-tailed test. In the case of a two-tailed test, the cut off sample scores is equal to +1.96 and -1.99 at the 5 per cent level.
Step 4: Outcome of Sample Score
The sample score is then converted into the Z value. Using the appropriate method of conversion this value is turned out to be equal to 2.
Step 5: Decision Making
The Z score value of 2 is far more than the cut off Z value, ie., +1.96, hence the result is significant, ie., rejection of the null hypothesis, i.e., there exists an association between the memory power and the consumption of the coffee before the test.
Click here , to understand hypothesis testing in detail.
Following are some real-life examples of hypothesis testing.
Hypothesis testing finds its application in the manufacturing processes such as in determining whether the implication of the new technique or process in the manufacturing plant caused the anomalies in the quality of the product or not. Let us suppose, that manufacturing plant X decides to verify that the particular method results in an increase in the defective products per quarter, say this number to be 200. Now, to verify this the researcher needs to calculate the mean of the number of defective products produced before the start and the end of the quarter.
Following is the representation of the Hypothesis testing of this example,
Null Hypothesis (Ho) : The average of the defective products produced is the same before and after the implementation of the new manufacturing method, i.e., μ after = μ before
Alternative Hypothesis (Ha) : The average number of defective products produced are different before and after the implementation of the new manufacturing method, i.e., μ after ≠ μ before
If the resultant p-value of the hypothesis testing comes lesser than the significant value, i.e., α = .05, then the null hypothesis is rejected and it can be concluded that the changes in the method of production lead to the rise in the number of defective products production per quarter.
Many businesses often use hypothesis testing to determine the impact of the newly implemented marketing techniques, campaigns or other tactics on the sales of the product. For example, the marketing department of the company assumed that if they spend more the digital advertisements it would lead to a rise in sales. To verify this assumption, the marketing department may raise the digital advertisement budget for a particular period, and then analyse the collected data at the end of that period. They have to perform hypothesis testing to verify their assumption. Here,
Null Hypothesis (Ho) : The average sales are the same before and after the rise in the digital advertisement budget, i.e., μafter = μbefore
Alternative Hypothesis (Ha) : The average sales increase after the rise in the digital advertisement budget, i.e., μafter > μbefore
If the P-value is smaller than the significant value (say .05), then the null hypothesis can be rejected by the marketing department, and they can conclude that the rise in the digital advertisement budget can result in a rise in the sales of the product.
Many pharmacists and doctors use hypothesis testing for clinical trials. The impact of the new clinical methods, medicines or procedures on the condition of the patients is analysed through hypothesis testing. For example, a pharmacist believes that the new medicine is resulting in the rise of blood pressure in diabetic patients. To test this assumption, the researcher has to measure the blood pressure of the sample patients (patients under investigation) before and after the intake of the new medicine for nearly a particular period say one month. The following procedure of the hypothesis testing is then followed,
Null Hypothesis (H0) : The average blood pressure is the same after and before the consumption of the medicine, i.e., μafter = μbefore
Alternative Hypothesis (Ha): The average blood pressure after the consumption of the medicine is less than the average blood pressure before the consumption of the medicine, i.e., μafter < μbefore
If the p-value of the hypothesis test is less than the significance value (say .o5), the null hypothesis is rejected, i.e., it can be concluded that the new drug is responsible for the rise in the blood pressure of diabetic patients.
Essential oils are gaining popularity nowadays due to their various benefits. Various essential oils such as ylang-ylang, lavender, and chamomile claim to reduce anxiety. You might like to test the true healing powers of all these essential oils. Suppose you assume that the lavender essential oil has the ability to reduce stress and anxiety. To check this assumption you may conduct the hypothesis testing by restating the hypothesis as follows,
Null Hypothesis (Ho) : Lavender essential has no effect on reducing anxiety.
Alternative Hypothesis (Ha): Lavender oil helps in reducing anxiety.
In this experiment, group A, i.e., the experimental group are provided with the lavender oil, while group B, i.e., the control group is provided with the placebo. The data is then collected using the various statistical tools and the stress level of both the groups, i.e., the experimental and the control group is then analysed. After the calculation, the significance level, and the p-value are found to be 0.25, and 0.05 respectively. The p values are less than the significance values, hence the null hypothesis is rejected, and it can be concluded that the lavender oil helps in reducing the stress among the people.
Nowadays, hypothesis testing is also used to examine the impact of pesticides, fertilizers, and other chemicals on the growth of plants or animals. Let us suppose a researcher wants to check his assumption that the particular fertilizer may result in the faster growth of the plant in a month than its usual growth of 10 inches. To verify this assumption he consistently gave that fertilizer to the plant for nearly a month. Following is the mathematical procedure of the hypothesis testing in this case,
Null Hypothesis (H0): The fertilizer does not have any influence on the growth of the plant. i.e., μ = 20 inches
Alternative Hypothesis (Ha): The fertiliser results in the faster growth of the plant, i.e., μ > 20 inches
Now, if the p-value of the hypothesis testing comes smaller than the level of significance, say .05, then the null hypothesis can be rejected, and you can conclude that the particular fertilizer is responsible for the faster plant growth.
Suppose the researcher assumes that Vitamin E helps in the faster growth of the Hair. He conduct an experiment in which the experimental group is provided with vitamin E for three months while the controlled group is provided with the placebo. The results are then analysed after the duration of three months. To verify his assumption he restates the hypothesis as follows,
Null Hypothesis (H0) : There is no association between the Vitamin E and the hair growth of the sample group, i.e., μafter = μbefore
Alternative Hypothesis (Ha) : The group of people who consumed the vitamin E shows faster hair growth than the average hair growth of them before the consumption of the Vitamin E provided other variables remains constant. Here, μafter > μbefore.
After performing the statistical analysis, the significance level and the p-value in this scenario are o.o5, and 0.20 respectively. Hence, the researcher can conclude that the consumption of vitamin E results in faster hair growth.
Suppose the two teachers say Mr X and Mr Y argue about the best teaching strategy. Mr X says that children will perform better in the annual exams if they are given the weekly tests, while Mr Y argues that the weekly test would not impact the performance of the children in the annual exams and it is waste of time. Now, to verify who is right between the both, we may conduct hypothesis testing. The researcher may formulate the hypothesis as follows,
Null Hypothesis (Ho): There is no association between the weekly tests on the performance of the children in the annual exams, i.e., the average marks scored by the children when they were given the weekly exams and when not, were the same. (μafter = μbefore)
Alternative Hypothesis (Ha): The children will perform better in the annual exams, when they have to give the weekly tests, rather than just giving the annual exams, i.e., μafter > μbefore.
Now, if the p-value of the hypothesis testing comes smaller than the level of significance, say .05, then the null hypothesis can be rejected, and the researcher can conclude that the children will perform better in the annual exams if the weekly examination system would be implemented.
Suppose a principle states that the students studying in her school have an IQ level of above average. To support her statement, the researcher may take a sample of around 50 random students from that school. Let’s say the average IQ score of those children is around 110, and the average IQ score of the mean population is 100 with a standard deviation of 15. The hypothesis testing is given as follows,
Null Hypothesis (Ho) : The population mean IQ score of 100 is a general fact, i.e., μ = 100.
Alternative Hypothesis (Ha): The average IQ score of the students is above average, i.e., μ > 100
It’s a one-tailed test as we are aiming for the ‘greater than’ assumption. Let us suppose the alpha level or we can say the significance level, in this case, is 5 per cent, i.e., 0.05, and this corresponds to the Z score equal to 1.645. The Z score is found by the statistical formula given by (112.5 – 100) / (15/√30) = 4.56. Now, the final step is to compare the values of the expected z score and the calculated z score. Here, the calculated Z score is lesser than the expected Z score, hence, the Null Hypothesis is rejected, i.e., the average IQ score of the children belonging to that school is above average.
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Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.
Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.
If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.
Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.
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To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.
A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”
Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.
When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.
The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.
As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.
In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.
Related: 9 Fundamental Data Science Skills for Business Professionals
1. alternative hypothesis and null hypothesis.
In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.
For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.
In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”
The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.
Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.
Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.
With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.
In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.
When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.
Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.
To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.
A survey involves asking a series of questions to a random population sample and recording self-reported responses.
Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.
Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.
Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.
If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.
Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .
Hypothesis Definition, Format, Examples, and Tips
Verywell / Alex Dos Diaz
Falsifiability of a hypothesis.
Hypotheses examples.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.
Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."
A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.
In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:
The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.
Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.
In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.
Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.
In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.
In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."
In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."
So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:
Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.
To form a hypothesis, you should take these steps:
In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.
Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.
One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.
A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.
Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.
For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.
These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.
One of the basic principles of any type of scientific research is that the results must be replicable.
Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.
Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.
To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.
The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:
A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .
The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."
Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.
Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.
Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.
Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).
Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.
The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.
Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607
Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z
Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004
Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691
Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18
Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.
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Hypothesis testing is the act of statistically evaluating a belief or theory. Hypothesis testing is the process of testing your theory using data from the real world obtained either through observation or experiments. Hypothesis testing is the step-by-step process of analyzing empirical data to check if it differs from the expected numbers if the belief or theory you started with was true.
This article walks you through the hypothesis testing concept and lists the process of hypothesis testing step by step.
To illustrate the concept and show you the hypothesis testing process with a example, we evaluate a belief that the companies in the Russell 3000 grow at a rate greater than 10% per year.
Here is a list of subtopics if you want to jump ahead:
Structuring the hypothesis test: the null and alternate hypothesis, the null hypothesis.
Computing the test statistic.
A hypothesis test and a criminal trial: similarities, the sampling distribution, reject region in hypothesis testing, some facts on the null hypothesis, some facts on the alternate hypothesis.
If you already know the concept of hypothesis testing concept and you only need to follow the step-by-step process outlined below.
List of Topics
A hypothesis test starts with a hypothesis that you want to test. It is designed as a statement or belief that you are examining. This statement or belief is termed the null hypothesis. The null hypothesis is what the hypothesis test is evaluating.
The opposite of the null hypothesis is called an alternate hypothesis. We are not examining the alternate hypothesis. Instead, the alternate hypothesis is what remains if the null hypothesis is rejected after being examined.
We will talk more about designing the null and alternate hypotheses later. Remember that we place what we want to prove in the alternate hypothesis. And we put the opposite of what we want to prove in the null hypothesis.
To continue our example, we will place what we believe to be true (mean growth rate is great than 10%) in the alternate hypothesis. And the opposite of the alternate hypothesis (mean growth rate is less than or equal to 10%) in the null hypothesis. Accordingly, we will have the following null and alternate hypotheses for our example: Ho: Mean growth rate <= 10% Ha: Mean growth rate > 10%
If we reject the null hypothesis, we will be concluding that the alternate hypothesis stands. On the other hand, if the evidence does not provide evidence to reject the null hypothesis, we can only conclude that we cannot reject the null hypothesis. In other words, we have not proven the alternate hypothesis. We conclude that we cannot reject the null hypothesis and therefore make no claim to have proven the alternate hypothesis or our starting theory or belief!
In hypothesis testing, the evidence required is gathered from a sample of the relevant population. Then, the parameter of interest from the sample is computed and referred to as the test statistic. This test statistic informs us about the null hypothesis.
Even if the null hypothesis is true, the test statistic is unlikely to be exactly equal to the parameter of interest of the true population because we are basing our test statistic on a sample of the population! A sample is only an unbiased estimator and not the actual population parameter. However, if the null hypothesis is true, the test statistic is likely to be close to the null hypothesis value, and likely agree with the null hypothesis. How close should it be? Or how far away from the null hypothesis value should the test statistic be before we can conclude that the null hypothesis is not true and “can be rejected”?
This is where the significance level comes into play. The significance level is the level of certainty required to reject the null hypothesis. The most commonly used significance levels are 1%, 5%, or 10% in practice. The significance level should be determined by the type of errors we are willing to tolerate (type 1 or type 2 errors).
We will use a 5% level of significance in our example today.
Significance level helps us determine the point beyond which we say that the null hypothesis is not true and “can be rejected”!
Best practice dictates that the critical value must be set up at the design stage and before the hypothesis test is done. The critical value is based on two factors. 1) the sampling distribution and 2) significance levels.
The sampling distribution is a distribution of sample values we can expect if the null hypothesis were true. Theoretically, the sample distribution is the distribution we would get if we took all possible samples that covered the entire population. The reason the sample distribution is central to hypothesis testing is that the mean of the sample distribution will equal the mean of the true population. So we use the sample distribution to evaluate the sample test statistic and check if our data agree with the null hypothesis.
If our null hypothesis is true, the test statistic will lie close to the middle of the sampling distribution. However, if our null hypothesis is NOT true, the test statistic will likely be closer to the tails of the sampling distribution.
To make a firm decision, we need a point beyond which we say that the null hypothesis is not true. That point is referred to as the critical value. The region beyond the critical value is referred to as the critical region or the reject region. If the test statistic falls in this region, we reject the null hypothesis. We conclude that the alternate hypothesis is true.
In our example, we are looking for a 5% confidence level. Therefore the critical value and reject region will be computed using a 5% confidence level. The critical value and reject region can be computed using the Z table, Microsoft Excel or another software program.
In Microsoft Excel we use the =NORM.S.INV(0.95) for a single tail critical value of 1.645 as the z value. We can use the Z table to arrive at the same value too.
Once we have the critical value, we run the experiment or gather sample data. Then, we analyze the sample data and compute the sample parameter of interest.
In our example, we randomly sample __ companies of the Russell 3000. We compute the average growth rates of the sample. We then compute the test statistic using this formula.
We compare the sample parameter of interest with the critical value/critical region. We are essentially checking if the test statistic falls in the reject region.
We are ready to conclude the hypothesis test only when we have the sample parameter of interest and the critical value at hand. We check if the parameter of interest falls in the critical regions identified in the earlier step.
In our example, we can see that the test statistic falls in the reject region.
If the parameter of interest falls in the critical regions, we reject the null hypothesis. Only when we reject the null hypothesis can we conclude that we believe the alternate hypothesis!
In our example, we can conclude that we reject the null hypothesis as the test statistic falls in the reject region. Because we reject the null hypothesis, we can say we believe the alternate hypothesis is true. And we conclude that the growth rate of companies of the Russell 3000 is greater than 10% per year!
A hypothesis test is often compared to and explained as a criminal trial. In a criminal trial, we start with the belief “innocent until proven guilty.” Similarly, in hypothesis testing, we assume that the null hypothesis is true. Therefore, we need to present data to disprove the null hypothesis. That is why we say that hypothesis testing is a trial of the null hypothesis. It is not the alternate hypothesis we are testing! The null hypothesis is similar to the criminal defendant. The data scientist is similar to the prosecutor. It is the prosecutor’s job to prove that the criminal is guilty. The prosecutor or the data scientist/researcher examines the data to present evidence that the null hypothesis is not true. Only if the researcher presents data to prove the null hypothesis is not true, can we conclude that that alternate hypothesis is true. If we do not have evidence to prove the criminal is guilty, he escapes conviction. It does not mean he is truly innocent. It only means that he was not found guilty. Similarly, if we do not have evidence to reject the null hypothesis we can only conclude that we cannot reject the null hypothesis.
We are looking for evidence that the null hypothesis is not true and “can be rejected”. This evidence is provided by a sample. How should this sample be gathered? How large should the sample be to provide this evidence? The sample must be carefully selected to be representative of the true population of interest. A random sample is best. Other sampling methods include cluster sampling, cluster sampling, stratified sampling, convenience sampling, etc. Each has its advantages and disadvantages, which we will not go into here.
Selecting the sample size is important in hypothesis testing. The sample size chosen impacts the risk of Type I and Type 2 errors. The sample size also directly determines the confidence levels and the power of the test. The sample size formula can be resorted to arrive at the sample size.
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Hypothesis testing in statistics involves testing an assumption about a population parameter using sample data. Learners can download Hypothesis Testing PDF to get instant access to all information!
What exactly is hypothesis testing, and how does it work in statistics? Can I find practical examples and understand the different types from this blog?
Hypothesis Testing : Ever wonder how researchers determine if a new medicine actually works or if a new marketing campaign effectively drives sales? They use hypothesis testing! It is at the core of how scientific studies, business experiments and surveys determine if their results are statistically significant or just due to chance.
Hypothesis testing allows us to make evidence-based decisions by quantifying uncertainty and providing a structured process to make data-driven conclusions rather than guessing. In this post, we will discuss hypothesis testing types, examples, and processes!
Table of Contents
Hypothesis testing is a statistical method used to evaluate the validity of a hypothesis using sample data. It involves assessing whether observed data provide enough evidence to reject a specific hypothesis about a population parameter.
Hypothesis testing in data science is a statistical method used to evaluate two mutually exclusive population statements based on sample data. The primary goal is to determine which statement is more supported by the observed data.
Hypothesis testing assists in supporting the certainty of findings in research and data science projects. This statistical inference aids in making decisions about population parameters using sample data. For those who are looking to deepen their knowledge in data science and expand their skillset, we highly recommend checking out Master Generative AI: Data Science Course by Physics Wallah .
Also Read: What is Encapsulation Explain in Details
The hypothesis testing procedure in data science involves a structured approach to evaluating hypotheses using statistical methods. Here’s a step-by-step breakdown of the typical procedure:
Also Read: Binary Search Algorithm
Hypothesis testing is a fundamental concept in statistics that aids analysts in making informed decisions based on sample data about a larger population. The process involves setting up two contrasting hypotheses, the null hypothesis and the alternative hypothesis, and then using statistical methods to determine which hypothesis provides a more plausible explanation for the observed data.
Once these hypotheses are established, analysts gather data from a sample and conduct statistical tests. The objective is to determine whether the observed results are statistically significant enough to reject the null hypothesis in favor of the alternative.
Hypothesis testing is a cornerstone in statistical analysis, providing a framework to evaluate the validity of assumptions or claims made about a population based on sample data. Within this framework, several specific tests are utilized based on the nature of the data and the question at hand. Here’s a closer look at the three fundamental types of hypothesis tests:
The z-test is a statistical method primarily employed when comparing means from two datasets, particularly when the population standard deviation is known. Its main objective is to ascertain if the means are statistically equivalent.
A crucial prerequisite for the z-test is that the sample size should be relatively large, typically 30 data points or more. This test aids researchers and analysts in determining the significance of a relationship or discovery, especially in scenarios where the data’s characteristics align with the assumptions of the z-test.
The t-test is a versatile statistical tool used extensively in research and various fields to compare means between two groups. It’s particularly valuable when the population standard deviation is unknown or when dealing with smaller sample sizes.
By evaluating the means of two groups, the t-test helps ascertain if a particular treatment, intervention, or variable significantly impacts the population under study. Its flexibility and robustness make it a go-to method in scenarios ranging from medical research to business analytics.
The Chi-Square test stands distinct from the previous tests, primarily focusing on categorical data rather than means. This statistical test is instrumental when analyzing categorical variables to determine if observed data aligns with expected outcomes as posited by the null hypothesis.
By assessing the differences between observed and expected frequencies within categorical data, the Chi-Square test offers insights into whether discrepancies are statistically significant. Whether used in social sciences to evaluate survey responses or in quality control to assess product defects, the Chi-Square test remains pivotal for hypothesis testing in diverse scenarios.
Also Read: Python vs Java: Which is Best for Machine learning algorithm
Hypothesis testing is a fundamental concept in statistics used to make decisions or inferences about a population based on a sample of data. The process involves setting up two competing hypotheses, the null hypothesis H 0 and the alternative hypothesis H 1.
Through various statistical tests, such as the t-test, z-test, or Chi-square test, analysts evaluate sample data to determine whether there’s enough evidence to reject the null hypothesis in favor of the alternative. The aim is to draw conclusions about population parameters or to test theories, claims, or hypotheses.
In research, hypothesis testing serves as a structured approach to validate or refute theories or claims. Researchers formulate a clear hypothesis based on existing literature or preliminary observations. They then collect data through experiments, surveys, or observational studies.
Using statistical methods, researchers analyze this data to determine if there’s sufficient evidence to reject the null hypothesis. By doing so, they can draw meaningful conclusions, make predictions, or recommend actions based on empirical evidence rather than mere speculation.
R, a powerful programming language and environment for statistical computing and graphics, offers a wide array of functions and packages specifically designed for hypothesis testing. Here’s how hypothesis testing is conducted in R:
Hypothesis testing is an integral part of statistics and research, offering a systematic approach to validate hypotheses. Leveraging R’s capabilities, researchers and analysts can efficiently conduct and interpret various hypothesis tests, ensuring robust and reliable conclusions from their data.
Yes, data scientists frequently engage in hypothesis testing as part of their analytical toolkit. Hypothesis testing is a foundational statistical technique used to make data-driven decisions, validate assumptions, and draw conclusions from data. Here’s how data scientists utilize hypothesis testing:
Let’s delve into some common examples of hypothesis testing and provide solutions or interpretations for each scenario.
Scenario : A coffee shop owner believes that the average waiting time for customers during peak hours is 5 minutes. To test this, the owner takes a random sample of 30 customer waiting times and wants to determine if the average waiting time is indeed 5 minutes.
Hypotheses :
Solution : Using a t-test (assuming population variance is unknown), calculate the t-statistic based on the sample mean, sample standard deviation, and sample size. Then, determine the p-value and compare it with a significance level (e.g., 0.05) to decide whether to reject the null hypothesis.
Scenario : An e-commerce company wants to determine if changing the color of a “Buy Now” button from blue to green increases the conversion rate.
Solution : Split website visitors into two groups: one sees the blue button (control group), and the other sees the green button (test group). Track the conversion rates for both groups over a specified period. Then, use a chi-square test or z-test (for large sample sizes) to determine if there’s a statistically significant difference in conversion rates between the two groups.
The formula for hypothesis testing typically depends on the type of test (e.g., z-test, t-test, chi-square test) and the nature of the data (e.g., mean, proportion, variance). Below are the basic formulas for some common hypothesis tests:
Z-Test for Population Mean :
Z=(σ/n)(xˉ−μ0)
T-Test for Population Mean :
t= (s/ n ) ( x ˉ −μ 0 )
s = Sample standard deviation
Chi-Square Test for Goodness of Fit :
χ2=∑Ei(Oi−Ei)2
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While you can perform hypothesis testing manually using the above formulas and statistical tables, many online tools and software packages simplify this process. Here’s how you might use a calculator or software:
When using any calculator or software, always ensure you understand the underlying assumptions of the test, interpret the results correctly, and consider the broader context of your research or analysis.
What are the key components of a hypothesis test.
The key components include: Null Hypothesis (H0): A statement of no effect or no difference. Alternative Hypothesis (H1 or Ha): A statement that contradicts the null hypothesis. Test Statistic: A value computed from the sample data to test the null hypothesis. Significance Level (α): The threshold for rejecting the null hypothesis. P-value: The probability of observing the given data, assuming the null hypothesis is true.
The significance level (often denoted as α) is the probability threshold used to determine whether to reject the null hypothesis. Commonly used values for α include 0.05, 0.01, and 0.10, representing a 5%, 1%, or 10% chance of rejecting the null hypothesis when it's actually true.
The choice between one-tailed and two-tailed tests depends on your research question and hypothesis. Use a one-tailed test when you're specifically interested in one direction of an effect (e.g., greater than or less than). Use a two-tailed test when you want to determine if there's a significant difference in either direction.
The p-value is a probability value that helps determine the strength of evidence against the null hypothesis. A low p-value (typically ≤ 0.05) suggests that the observed data is inconsistent with the null hypothesis, leading to its rejection. Conversely, a high p-value suggests that the data is consistent with the null hypothesis, leading to no rejection.
No, hypothesis testing cannot prove a hypothesis true. Instead, it helps assess the likelihood of observing a given set of data under the assumption that the null hypothesis is true. Based on this assessment, you either reject or fail to reject the null hypothesis.
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Table of Contents
As per the definition from Oxford languages, a hypothesis is a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation. As per the Dictionary page on Hypothesis , Hypothesis means a proposition or set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide investigation (working hypothesis) or accepted as highly probable in the light of established facts.
The hypothesis can be defined as the claim that can either be related to the truth about something that exists in the world, or, truth about something that’s needs to be established a fresh . In simple words, another word for the hypothesis is the “claim” . Until the claim is proven to be true, it is called the hypothesis. Once the claim is proved, it becomes the new truth or new knowledge about the thing. For example , let’s say that a claim is made that students studying for more than 6 hours a day gets more than 90% of marks in their examination. Now, this is just a claim or a hypothesis and not the truth in the real world. However, in order for the claim to become the truth for widespread adoption, it needs to be proved using pieces of evidence, e.g., data. In order to reject this claim or otherwise, one needs to do some empirical analysis by gathering data samples and evaluating the claim. The process of gathering data and evaluating the claims or hypotheses with the goal to reject or otherwise (failing to reject) can be called as hypothesis testing . Note the wordings – “failing to reject”. It means that we don’t have enough evidence to reject the claim. Thus, until the time that new evidence comes up, the claim can be considered the truth. There are different techniques to test the hypothesis in order to reach the conclusion of whether the hypothesis can be used to represent the truth of the world.
One must note that the hypothesis testing never constitutes a proof that the hypothesis is absolute truth based on the observations. It only provides added support to consider the hypothesis as truth until the time that new evidences can against the hypotheses can be gathered. We can never be 100% sure about truth related to those hypotheses based on the hypothesis testing.
Simply speaking, hypothesis testing is a framework that can be used to assert whether the claim or the hypothesis made about a real-world/real-life event can be seen as the truth or otherwise based on the given data (evidences).
Before we get ahead and start understanding more details about hypothesis and hypothesis testing steps, lets take a look at some real-world examples of how to think about hypothesis and hypothesis testing when dealing with real-world problems :
You may note different hypotheses which are listed above. The next step would be validate some of these hypotheses. This is where data scientists will come into picture. One or more data scientists may be asked to work on different hypotheses. This would result in these data scientists looking for appropriate data related to the hypothesis they are working. This section will be detailed out in near future.
The first step to hypothesis testing is defining or stating a hypothesis. Before the hypothesis can be tested, we need to formulate the hypothesis in terms of mathematical expressions. There are two important aspects to pay attention to, prior to the formulation of the hypothesis. The following represents different types of hypothesis that could be put to hypothesis testing:
Based on the above considerations, the following hypothesis can be stated for doing hypothesis testing.
Once the hypothesis is defined or stated, the next step is to formulate the null and alternate hypothesis in order to begin hypothesis testing as described above.
In the case where the given statement is a well-established fact or default state of being in the real world, one can call it a null hypothesis (in the simpler word, nothing new). Well-established facts don’t need any hypothesis testing and hence can be called the null hypothesis. In cases, when there are any new claims made which is not well established in the real world, the null hypothesis can be thought of as the default state or opposite state of that claim. For example , in the previous section, the claim or hypothesis is made that the students studying for more than 6 hours a day gets more than 90% of marks in their examination. The null hypothesis, in this case, will be that the claim is not true or real. The null hypothesis can be stated that there is no relationship or association between the students reading more than 6 hours a day and they getting 90% of the marks. Any occurrence is only a chance occurrence. Another example of hypothesis is when somebody is alleged that they have performed a crime.
Null hypothesis is denoted by letter H with 0, e.g., [latex]H_0[/latex]
When the given statement is a claim (unexpected event in the real world) and not yet proven, one can call/formulate it as an alternate hypothesis and accordingly define a null hypothesis which is the opposite state of the hypothesis. The alternate hypothesis is a new knowledge or truth that needs to be established. In simple words, the hypothesis or claim that needs to be tested against reality in the real world can be termed the alternate hypothesis. In order to reach a conclusion that the claim (alternate hypothesis) can be considered the new knowledge or truth (based on the available evidence), it would be important to reject the null hypothesis. It should be noted that null and alternate hypotheses are mutually exclusive and at the same time asymmetric. In the example given in the previous section, the claim that the students studying for more than 6 hours get more than 90% of marks can be termed as the alternate hypothesis.
Alternate hypothesis is denoted with H subscript a, e.g., [latex]H_a[/latex]
Once the hypothesis is formulated as null([latex]H_0[/latex]) and alternate hypothesis ([latex]H_a[/latex]), there are two possible outcomes that can happen from hypothesis testing. These outcomes are the following:
The following are some examples of the null and alternate hypothesis.
The weight of the sugar packet is 500 gm. (A well-established fact) | |
The weight of the sugar packet is 500 gm. |
Running 5 miles a day result in the reduction of 10 kg of weight within a month. | |
Running 5 miles a day results in the reduction of 10 kg of weight within a month. |
The housing price depend upon the average income of people staying in the locality. | |
The housing price depends upon the average income of people staying in the locality. |
Here is the diagram which represents the workflow of Hypothesis Testing.
Figure 1. Hypothesis Testing Steps
Based on the above, the following are some of the steps to be taken when doing hypothesis testing:
Once you formulate the hypotheses, there is the need to test those hypotheses. Meaning, say that the null hypothesis is stated as the statement that housing price does not depend upon the average income of people staying in the locality, it would be required to be tested by taking samples of housing prices and, based on the test results, this Null hypothesis could either be rejected or failed to be rejected . In hypothesis testing, the following two are the outcomes:
Take the above example of the sugar packet weighing 500 gm. The Null hypothesis is set as the statement that the sugar packet weighs 500 gm. After taking a sample of 20 sugar packets and testing/taking its weight, it was found that the average weight of the sugar packets came to 495 gm. The test statistics (t-statistics) were calculated for this sample and the P-value was determined. Let’s say the P-value was found to be 15%. Assuming that the level of significance is selected to be 5%, the test statistic is not statistically significant (P-value > 5%) and thus, the null hypothesis fails to get rejected. Thus, one could safely conclude that the sugar packet does weigh 500 gm. However, if the average weight of canned sauce would have found to be 465 gm, this is way beyond/away from the mean value of 500 gm and one could have ended up rejecting the Null Hypothesis based on the P-value .
Hypothesis testing can be applied in both problem analysis and solution implementation. The following represents method on how you can apply hypothesis testing technique for both problem and solution space:
The claim that needs to be established is set as ____________, the outcome of hypothesis testing is _________.
Please select 2 correct answers
There is a claim that doing pranayama yoga results in reversing diabetes. which of the following is true about null hypothesis.
In this post, you learned about hypothesis testing and related nuances such as the null and alternate hypothesis formulation techniques, ways to go about doing hypothesis testing etc. In data science, one of the reasons why one needs to understand the concepts of hypothesis testing is the need to verify the relationship between the dependent (response) and independent (predictor) variables. One would, thus, need to understand the related concepts such as hypothesis formulation into null and alternate hypothesis, level of significance, test statistics calculation, P-value, etc. Given that the relationship between dependent and independent variables is a sort of hypothesis or claim , the null hypothesis could be set as the scenario where there is no relationship between dependent and independent variables.
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Understanding p-value.
Yarilet Perez is an experienced multimedia journalist and fact-checker with a Master of Science in Journalism. She has worked in multiple cities covering breaking news, politics, education, and more. Her expertise is in personal finance and investing, and real estate.
In statistics, a p-value is defined as In statistics, a p-value indicates the likelihood of obtaining a value equal to or greater than the observed result if the null hypothesis is true.
The p-value serves as an alternative to rejection points to provide the smallest level of significance at which the null hypothesis would be rejected. A smaller p-value means stronger evidence in favor of the alternative hypothesis.
P-value is often used to promote credibility for studies or reports by government agencies. For example, the U.S. Census Bureau stipulates that any analysis with a p-value greater than 0.10 must be accompanied by a statement that the difference is not statistically different from zero. The Census Bureau also has standards in place stipulating which p-values are acceptable for various publications.
Jessica Olah / Investopedia
P-values are usually found using p-value tables or spreadsheets/statistical software. These calculations are based on the assumed or known probability distribution of the specific statistic tested. The sample size, which determines the reliability of the observed data, directly influences the accuracy of the p-value calculation. he p-value approach to hypothesis testing uses the calculated he p-value approach to hypothesis testing uses the calculated P-values are calculated from the deviation between the observed value and a chosen reference value, given the probability distribution of the statistic, with a greater difference between the two values corresponding to a lower p-value.
Mathematically, the p-value is calculated using integral calculus from the area under the probability distribution curve for all values of statistics that are at least as far from the reference value as the observed value is, relative to the total area under the probability distribution curve. Standard deviations, which quantify the dispersion of data points from the mean, are instrumental in this calculation.
The calculation for a p-value varies based on the type of test performed. The three test types describe the location on the probability distribution curve: lower-tailed test, upper-tailed test, or two-tailed test . In each case, the degrees of freedom play a crucial role in determining the shape of the distribution and thus, the calculation of the p-value.
In a nutshell, the greater the difference between two observed values, the less likely it is that the difference is due to simple random chance, and this is reflected by a lower p-value.
The p-value approach to hypothesis testing uses the calculated probability to determine whether there is evidence to reject the null hypothesis. This determination relies heavily on the test statistic, which summarizes the information from the sample relevant to the hypothesis being tested. The null hypothesis, also known as the conjecture, is the initial claim about a population (or data-generating process). The alternative hypothesis states whether the population parameter differs from the value of the population parameter stated in the conjecture.
In practice, the significance level is stated in advance to determine how small the p-value must be to reject the null hypothesis. Because different researchers use different levels of significance when examining a question, a reader may sometimes have difficulty comparing results from two different tests. P-values provide a solution to this problem.
Even a low p-value is not necessarily proof of statistical significance, since there is still a possibility that the observed data are the result of chance. Only repeated experiments or studies can confirm if a relationship is statistically significant.
For example, suppose a study comparing returns from two particular assets was undertaken by different researchers who used the same data but different significance levels. The researchers might come to opposite conclusions regarding whether the assets differ.
If one researcher used a confidence level of 90% and the other required a confidence level of 95% to reject the null hypothesis, and if the p-value of the observed difference between the two returns was 0.08 (corresponding to a confidence level of 92%), then the first researcher would find that the two assets have a difference that is statistically significant , while the second would find no statistically significant difference between the returns.
To avoid this problem, the researchers could report the p-value of the hypothesis test and allow readers to interpret the statistical significance themselves. This is called a p-value approach to hypothesis testing. Independent observers could note the p-value and decide for themselves whether that represents a statistically significant difference or not.
An investor claims that their investment portfolio’s performance is equivalent to that of the Standard & Poor’s (S&P) 500 Index . To determine this, the investor conducts a two-tailed test.
The null hypothesis states that the portfolio’s returns are equivalent to the S&P 500’s returns over a specified period, while the alternative hypothesis states that the portfolio’s returns and the S&P 500’s returns are not equivalent—if the investor conducted a one-tailed test , the alternative hypothesis would state that the portfolio’s returns are either less than or greater than the S&P 500’s returns.
The p-value hypothesis test does not necessarily make use of a preselected confidence level at which the investor should reset the null hypothesis that the returns are equivalent. Instead, it provides a measure of how much evidence there is to reject the null hypothesis. The smaller the p-value, the greater the evidence against the null hypothesis.
Thus, if the investor finds that the p-value is 0.001, there is strong evidence against the null hypothesis, and the investor can confidently conclude that the portfolio’s returns and the S&P 500’s returns are not equivalent.
Although this does not provide an exact threshold as to when the investor should accept or reject the null hypothesis, it does have another very practical advantage. P-value hypothesis testing offers a direct way to compare the relative confidence that the investor can have when choosing among multiple different types of investments or portfolios relative to a benchmark such as the S&P 500.
For example, for two portfolios, A and B, whose performance differs from the S&P 500 with p-values of 0.10 and 0.01, respectively, the investor can be much more confident that portfolio B, with a lower p-value, will actually show consistently different results.
A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.
A p-value of 0.001 indicates that if the null hypothesis tested were indeed true, then there would be a one-in-1,000 chance of observing results at least as extreme. This leads the observer to reject the null hypothesis because either a highly rare data result has been observed or the null hypothesis is incorrect.
If you have two different results, one with a p-value of 0.04 and one with a p-value of 0.06, the result with a p-value of 0.04 will be considered more statistically significant than the p-value of 0.06. Beyond this simplified example, you could compare a 0.04 p-value to a 0.001 p-value. Both are statistically significant, but the 0.001 example provides an even stronger case against the null hypothesis than the 0.04.
The p-value is used to measure the significance of observational data. When researchers identify an apparent relationship between two variables, there is always a possibility that this correlation might be a coincidence. A p-value calculation helps determine if the observed relationship could arise as a result of chance.
U.S. Census Bureau. “ Statistical Quality Standard E1: Analyzing Data .”
Ai generator.
Navigating the intricacies of research begins with crafting well-defined research questions and hypothesis statements. These essential components guide the entire research process, shaping investigations and analyses. In this comprehensive guide, explore the art of formulating research questions and hypothesis statements. Learn how to create focused, inquiry-driven questions and construct research hypothesis statements that capture the essence of your study. Unveil examples and invaluable tips to enhance your research endeavors.
Research Question: How does regular exercise impact the mental well-being of college students?
Hypothesis Statement: College students who engage in regular exercise experience improved mental well-being compared to those who do not exercise regularly.
In this example, the research question focuses on the relationship between exercise and mental well-being among college students. The hypothesis statement predicts a specific outcome, stating that there will be a positive impact on mental well-being for those who exercise regularly. The hypothesis guides the research process and provides a clear expectation for the study’s results.
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Education | How does the integration of technology impact student engagement in elementary classrooms? | Elementary students exposed to technology-enhanced lessons exhibit higher levels of engagement. |
Health | What is the relationship between sleep quality and stress levels among working professionals? | Working professionals who experience higher sleep quality report lower levels of stress. |
Environment | How does exposure to urban green spaces influence residents’ mental well-being? | Residents with regular access to urban green spaces exhibit higher levels of mental well-being. |
Economics | What impact does minimum wage increase have on small business profitability? | Small businesses in regions with minimum wage increases experience decreased profitability. |
Social Media | How do social media influencers affect consumer purchasing decisions? | Consumers are more likely to make decisions based on recommendations from social media influencers. |
Gender Studies | What is the perception of gender roles among adolescents in a multicultural society? | Adolescents in multicultural societies have fluid perceptions of traditional gender roles. |
Nutrition | Is there a correlation between diet quality and academic performance among college students? | College students with healthier diets show better academic performance. |
Political Science | How does media framing influence public opinion on climate change policies? | Media framing significantly impacts public opinion on climate change policies. |
Criminal Justice | What factors contribute to recidivism rates among juvenile offenders? | Juvenile offenders with strong support systems are less likely to engage in recidivism. |
Cultural Studies | How does exposure to diverse cultural experiences impact cultural sensitivity among students? | Students engaging in diverse cultural experiences develop higher cultural sensitivity. |
Technology Adoption | What factors influence the adoption of e-commerce platforms among older adults? | Older adults with higher digital literacy levels are more likely to adopt e-commerce platforms. |
Language Acquisition | How does bilingualism impact cognitive development in children? | Bilingual children exhibit enhanced cognitive flexibility and problem-solving skills. |
Urban Planning | What are the effects of green infrastructure on urban heat island mitigation? | Urban areas with green infrastructure experience lower temperatures during heatwaves. |
Parenting Styles | What role does authoritative parenting play in adolescent self-esteem development? | Adolescents raised by authoritative parents tend to have higher self-esteem levels. |
Workplace Diversity | How does workplace diversity impact employee satisfaction and job performance? | Diverse workforces lead to higher employee satisfaction and improved job performance. |
Cultural Influence on Perception | How do cultural backgrounds affect individuals’ perception of facial expressions? | Cultural backgrounds influence how individuals interpret facial expressions. |
Music and Mood | Does listening to music of different genres have varying effects on mood regulation? | Different music genres evoke distinct emotional responses, influencing mood regulation. |
Advertising Effectiveness | What factors contribute to the effectiveness of online banner advertisements? | Personalized online banner ads with compelling visuals are more effective in user engagement. |
Relationship Satisfaction | How does communication style affect relationship satisfaction among couples? | Open and empathetic communication leads to higher relationship satisfaction among couples. |
Cultural Identity and Mental Health | How does the integration of cultural identity influence mental health outcomes among immigrants? | Immigrant adolescents who maintain cultural identity tend to exhibit better mental health. |
Education | How does the integration of technology impact student engagement in elementary classrooms? | Elementary students exposed to technology-enhanced lessons exhibit higher levels of engagement. |
Health | What is the relationship between sleep quality and stress levels among working professionals? | Working professionals who experience higher sleep quality report lower levels of stress. |
Environment | How does exposure to urban green spaces influence residents’ mental well-being? | Residents with regular access to urban green spaces exhibit higher levels of mental well-being. |
Economics | What impact does minimum wage increase have on small business profitability? | Small businesses in regions with minimum wage increases experience decreased profitability. |
Social Media | How do social media influencers affect consumer purchasing decisions? | Consumers are more likely to make decisions based on recommendations from social media influencers. |
Gender Studies | What is the perception of gender roles among adolescents in a multicultural society? | Adolescents in multicultural societies have fluid perceptions of traditional gender roles. |
Nutrition | Is there a correlation between diet quality and academic performance among college students? | College students with healthier diets show better academic performance. |
Political Science | How does media framing influence public opinion on climate change policies? | Media framing significantly impacts public opinion on climate change policies. |
Criminal Justice | What factors contribute to recidivism rates among juvenile offenders? | Juvenile offenders with strong support systems are less likely to engage in recidivism. |
Cultural Studies | How does exposure to diverse cultural experiences impact cultural sensitivity among students? | Students engaging in diverse cultural experiences develop higher cultural sensitivity. |
Technology Adoption | What factors influence the adoption of e-commerce platforms among older adults? | Older adults with higher digital literacy levels are more likely to adopt e-commerce platforms. |
Language Acquisition | How does bilingualism impact cognitive development in children? | Bilingual children exhibit enhanced cognitive flexibility and problem-solving skills. |
Urban Planning | What are the effects of green infrastructure on urban heat island mitigation? | Urban areas with green infrastructure experience lower temperatures during heatwaves. |
Parenting Styles | What role does authoritative parenting play in adolescent self-esteem development? | Adolescents raised by authoritative parents tend to have higher self-esteem levels. |
Workplace Diversity | How does workplace diversity impact employee satisfaction and job performance? | Diverse workforces lead to higher employee satisfaction and improved job performance. |
Cultural Influence on Perception | How do cultural backgrounds affect individuals’ perception of facial expressions? | Cultural backgrounds influence how individuals interpret facial expressions. |
Music and Mood | Does listening to music of different genres have varying effects on mood regulation? | Different music genres evoke distinct emotional responses, influencing mood regulation. |
Advertising Effectiveness | What factors contribute to the effectiveness of online banner advertisements? | Personalized online banner ads with compelling visuals are more effective in user engagement. |
Relationship Satisfaction | How does communication style affect relationship satisfaction among couples? | Open and empathetic communication leads to higher relationship satisfaction among couples. |
Cultural Identity and Mental Health | How does the integration of cultural identity influence mental health outcomes among immigrants? | Immigrant adolescents who maintain cultural identity tend to exhibit better mental health. |
Educational Psychology | How does feedback delivery method affect students’ motivation in online learning environments? | Students receiving personalized feedback in online courses show higher motivation levels. |
Healthcare Access | What factors influence individuals’ access to quality healthcare services in rural areas? | Rural residents with reliable transportation options have better access to quality healthcare. |
Environmental Impact | How does deforestation impact biodiversity in tropical rainforests? | Increased rates of deforestation lead to a decline in biodiversity within tropical rainforests. |
Consumer Behavior | What role do product reviews play in consumers’ purchasing decisions on e-commerce platforms? | Consumers are more likely to choose products with positive reviews when shopping online. |
Language Perception | How does language fluency affect individuals’ perception of different accents? | Individuals fluent in a language are more likely to accurately differentiate between accents. |
Food Preferences | What factors contribute to the preference for spicy foods among certain cultural groups? | Cultural background significantly influences the preference for spicy foods among individuals. |
Urban Mobility | How does the availability of public transportation impact car usage in urban areas? | Cities with efficient public transportation systems experience reduced car usage by residents. |
Political Engagement | What factors determine young adults’ engagement in political activities? | Young adults with higher levels of education tend to be more engaged in political activities. |
Artificial Intelligence in Finance | How does the integration of AI-based algorithms impact stock trading accuracy? | AI algorithms improve stock trading accuracy when integrated into financial trading systems. |
Body Image Perception | How does exposure to idealized body images in media influence individuals’ self-perception? | Individuals exposed to idealized body images in media tend to have lower self-esteem levels. |
Technology Adoption | How does user interface design impact the adoption rate of mobile applications? | Mobile applications with intuitive user interfaces are more likely to have higher adoption rates. |
Cultural Influence on Education | How does cultural background affect students’ learning preferences and styles? | Students from different cultural backgrounds have varied learning preferences and styles. |
Economic Development | What role does foreign direct investment play in the economic growth of developing countries? | Developing countries with higher foreign direct investment experience greater economic growth. |
Social Interaction in Virtual Reality | How does virtual reality impact social interaction and communication among users? | Users of virtual reality platforms tend to experience enhanced social interaction and communication. |
Body-Mind Connection | What is the relationship between physical exercise and cognitive functioning in elderly adults? | Elderly adults who engage in regular physical exercise exhibit better cognitive functioning. |
Political Polarization | How does exposure to partisan media influence individuals’ political views? | Exposure to partisan media significantly shapes and reinforces individuals’ political views. |
Work-Life Balance | What factors contribute to employees’ perception of work-life balance in corporate settings? | Employees with flexible work arrangements tend to perceive better work-life balance. |
Genetic Influence on Behavior | To what extent does genetic predisposition influence risk-taking behavior in individuals? | Individuals with a genetic predisposition to risk-taking behavior are more likely to exhibit such behavior. |
Media Representation of Gender | How are gender roles and stereotypes portrayed in children’s animated television shows? | Children’s animated television shows often perpetuate traditional gender roles and stereotypes. |
Economic Inequality | What is the relationship between income inequality and social mobility in urban areas? | Urban areas with higher income inequality tend to have lower social mobility rates. |
Nutrition and Cognitive Function | How does dietary intake influence cognitive function in school-aged children? | School-aged children with balanced diets tend to exhibit better cognitive function. |
Technology Addiction | How does excessive smartphone usage impact individuals’ overall well-being? | Excessive smartphone usage is negatively correlated with individuals’ overall well-being. |
Creativity and Age | How does age influence individuals’ creativity and innovation levels? | Creativity and innovation levels tend to decrease with advancing age. |
Online Learning Effectiveness | What factors determine the effectiveness of online learning compared to traditional classroom learning? | Online learning is equally effective as traditional classroom learning in academic outcomes. |
Media Exposure and Body Image | How does exposure to digitally altered images in media impact body image dissatisfaction among adolescents? | Adolescents exposed to digitally altered images in media are more likely to experience body image dissatisfaction. |
Motivation in the Workplace | How does recognition and rewards affect employees’ motivation in the workplace? | Employees who receive regular recognition and rewards tend to exhibit higher levels of motivation. |
Social Media and Mental Health | What is the relationship between social media usage and mental health among adolescents? | Adolescents who spend excessive time on social media platforms tend to experience poorer mental health. |
Artistic Expression and Emotion | How does artistic expression influence emotional expression and regulation in individuals? | Individuals engaged in artistic activities tend to have enhanced emotional expression and regulation. |
Cultural Diversity in Education | How does a diverse teaching staff impact students’ cultural awareness and understanding? | Schools with a diverse teaching staff promote greater cultural awareness and understanding among students. |
Economic Impact of Tourism | What is the economic impact of tourism on local communities and businesses? | Tourism significantly contributes to the economic growth of local communities and businesses. |
Social Media and Self-Esteem | How does social media usage impact adolescents’ self-esteem and body image? | Adolescents who spend more time on social media platforms are more likely to experience lower self-esteem and body image issues. |
Gender Wage Gap | What factors contribute to the gender wage gap in the corporate sector? | Gender wage gaps in the corporate sector can be attributed to disparities in job roles, negotiation skills, and workplace biases. |
Influence of Parenting Styles | How do different parenting styles influence adolescents’ academic achievement? | Adolescents raised in authoritative parenting environments tend to achieve higher academic success compared to other styles. |
Peer Pressure and Risk Behavior | How does peer pressure influence risk behaviors among teenagers? | Teenagers who succumb to peer pressure are more likely to engage in risky behaviors, such as substance abuse and delinquency. |
Media Exposure and Violence | Is there a link between exposure to violent media and aggressive behavior in children? | Children exposed to violent media content are more likely to exhibit aggressive behaviors in real-life situations. |
Advertising Appeals | How do emotional appeals versus rational appeals influence consumer purchasing decisions? | Consumers are more likely to make emotional purchasing decisions when exposed to emotional advertising appeals. |
Work-Related Stress and Health | How does work-related stress impact employees’ physical and mental health? | Employees experiencing high levels of work-related stress are more prone to physical and mental health issues. |
Social Support and Mental Health | What role does social support play in promoting positive mental health outcomes? | Individuals with strong social support networks tend to exhibit better mental health outcomes and coping mechanisms. |
Impact of Music on Memory | Can listening to music improve memory recall in learning environments? | Background music with a moderate tempo and melody can enhance memory recall in learning environments. |
Urbanization and Air Quality | How does rapid urbanization affect air quality in metropolitan areas? | Rapid urbanization is associated with deteriorating air quality due to increased pollution levels in metropolitan areas. |
Impact of Social Media on Relationships | How does frequent social media use influence the quality of romantic relationships among young adults? | Young adults who spend more time on social media tend to have lower relationship satisfaction and communication. |
Cultural Diversity and Workplace | What is the impact of cultural diversity on workplace productivity and collaboration? | Workplaces that embrace cultural diversity experience increased productivity and better collaboration among employees. |
Technology and Academic Performance | How does the use of digital devices affect students’ academic performance in classrooms? | Students who use digital devices excessively during classes tend to have lower academic performance compared to those who limit usage. |
Influence of Family Structure | How does family structure influence adolescents’ emotional development and well-being? | Adolescents from single-parent households exhibit higher levels of emotional distress compared to those from two-parent households. |
Personality Traits and Leadership | What personality traits contribute to effective leadership in various organizational contexts? | Leaders with high levels of extroversion, emotional intelligence, and adaptability tend to be more effective in guiding teams and organizations. |
Exercise and Mental Health | Does regular exercise have a positive impact on individuals’ mental health and well-being? | Regular physical exercise is associated with improved mental health outcomes and reduced symptoms of anxiety and depression. |
Social Media and Political Engagement | How does social media usage influence individuals’ participation in political discussions and activities? | Individuals who engage in political discussions on social media are more likely to actively participate in offline political activities. |
Stress and Sleep Quality | How does chronic stress affect sleep quality and patterns in adults? | Adults experiencing chronic stress tend to have disrupted sleep patterns and lower sleep quality compared to those with lower stress levels. |
Role of Nutrition in Aging | What role does nutrition play in slowing down the aging process and promoting healthy aging? | Individuals who consume a diet rich in antioxidants and nutrients tend to experience slower aging and better overall health in older age. |
Gender Stereotypes in STEM Fields | How do gender stereotypes influence individuals’ career choices in STEM fields (science, technology, engineering, mathematics)? | Gender stereotypes contribute to the underrepresentation of women in STEM fields by discouraging their pursuit of STEM careers. |
Social Media and Body Image | What is the relationship between social media usage and body dissatisfaction among adolescents? | Adolescents who spend more time on social media platforms are more likely to experience negative body image and dissatisfaction. |
Impact of Arts Education on Creativity | How does participation in arts education programs influence students’ creative thinking skills? | Students who engage in arts education programs tend to exhibit enhanced creative thinking skills compared to those who do not. |
Urban Green Spaces and Mental Health | How do urban green spaces impact individuals’ mental health and well-being? | Access to urban green spaces is positively correlated with improved mental health outcomes and reduced stress levels among urban residents. |
Technology Use and Academic Achievement | How does the amount of time spent on digital devices impact students’ academic achievement? | Students who excessively use digital devices for non-academic purposes tend to have lower academic achievement compared to those who limit usage. |
Impact of Social Support on Recovery | Does having a strong social support system aid in the recovery process after major surgeries? | Patients with robust social support networks tend to experience faster recovery and better postoperative outcomes following major surgeries. |
Impact of Parental Involvement in Education | How does parental involvement affect students’ academic performance and motivation? | Students with actively involved parents tend to have higher academic performance and greater motivation in school. |
Influence of Peer Feedback on Learning | Does receiving peer feedback enhance students’ learning outcomes in collaborative projects? | Students who receive constructive peer feedback during collaborative projects show improved learning outcomes. |
Music and Stress Reduction | Can listening to music help reduce stress levels in high-stress work environments? | Employees who listen to soothing music during work breaks experience reduced stress and increased relaxation. |
Effects of Sleep on Memory | How does sleep duration impact memory consolidation and recall in college students? | College students with sufficient sleep duration tend to exhibit better memory consolidation and recall abilities. |
Cultural Sensitivity in Healthcare | How does cultural sensitivity training impact healthcare providers’ patient communication? | Healthcare providers who undergo cultural sensitivity training exhibit improved patient communication and trust. |
Impact of Outdoor Play on Child Development | Does outdoor play contribute to better motor skills and cognitive development in young children? | Young children who engage in outdoor play activities demonstrate improved motor skills and cognitive development. |
Relationship Between Diet and Heart Health | What is the connection between dietary habits and the risk of cardiovascular diseases? | Individuals with a diet high in saturated fats and sodium have an increased risk of cardiovascular diseases. |
Impact of Classroom Design on Learning | How does classroom design influence students’ engagement and learning outcomes in schools? | Classroom designs with flexible seating and interactive elements foster increased student engagement and learning. |
Technology Use and Family Communication | How does technology use affect family communication patterns and relationships? | Families that excessively rely on technology for communication experience reduced quality in family relationships. |
Motivation and Employee Productivity | How does intrinsic motivation impact employee productivity in the workplace? | Employees who are intrinsically motivated tend to exhibit higher levels of productivity in their work tasks. |
Impact of Nutrition on Cognitive Function | Can a balanced diet improve cognitive function and concentration in older adults? | Older adults with a balanced diet rich in antioxidants and nutrients tend to experience improved cognitive function. |
Factors Affecting Online Shopping Behavior | What factors influence consumers’ decision-making in online shopping? | Consumers’ online shopping behavior is influenced by factors such as price, reviews, convenience, and website design. |
Effectiveness of Online Learning Platforms | How effective are online learning platforms in enhancing students’ knowledge retention and engagement? | Students who use interactive online learning platforms show higher levels of knowledge retention and engagement. |
Media Exposure and Political Beliefs | Does media exposure shape individuals’ political beliefs and opinions? | Individuals exposed to polarized media content tend to develop more extreme political beliefs and opinions. |
Impact of Meditation on Stress Reduction | How does regular meditation practice contribute to stress reduction and mental well-being? | Regular meditation practice is associated with decreased stress levels and improved mental well-being in individuals. |
Social Media Influencer Marketing | What is the impact of social media influencer marketing on consumer purchasing decisions? | Consumers influenced by social media influencers are more likely to make purchasing decisions based on their recommendations. |
Factors Influencing Job Satisfaction | What factors contribute to employees’ job satisfaction in the workplace? | Employees’ job satisfaction is influenced by factors such as work-life balance, compensation, recognition, and job security. |
Impact of Early Childhood Education | How does early childhood education affect cognitive development and school readiness? | Children who receive quality early childhood education tend to demonstrate enhanced cognitive development and school readiness. |
Effects of Exercise on Mental Health | Can regular physical exercise improve mental health and reduce symptoms of anxiety and depression? | Individuals who engage in regular exercise experience improved mental health outcomes and reduced symptoms of anxiety and depression. |
Impact of Social Media on Self-Esteem | Does excessive social media use contribute to lower self-esteem levels among adolescents? | Adolescents who spend more time on social media platforms tend to have lower self-esteem compared to those who limit usage. |
Effects of Video Games on Aggression | What is the relationship between violent video game exposure and aggressive behavior in adolescents? | Adolescents exposed to violent video games are more likely to exhibit aggressive behavior compared to those who are not exposed. |
Impact of Gender Diversity on Team Performance | How does gender diversity influence team performance in corporate settings? | Teams with diverse gender compositions tend to achieve higher levels of performance compared to less diverse teams. |
Effect of Music Tempo on Consumer Behavior | Does music tempo influence consumers’ shopping behavior in retail stores? | Retail stores playing fast-tempo music tend to experience increased sales due to consumers’ faster shopping behavior. |
Influence of Parenting Style on Academic Success | How do different parenting styles impact students’ academic success and motivation? | Students raised in authoritative households tend to exhibit higher academic success and intrinsic motivation in school. |
Impact of Gender Stereotypes on Career Choices | How do gender stereotypes affect individuals’ career choices in traditionally male-dominated fields? | Individuals exposed to gender stereotypes are less likely to pursue careers in traditionally male-dominated fields. |
Effects of Climate Change on Ecosystems | What are the consequences of climate change on ecosystems and biodiversity? | Ecosystems exposed to rising temperatures experience shifts in species distribution and increased threats to biodiversity. |
Influence of Peer Pressure on Risky Behavior | How does peer pressure influence adolescents’ engagement in risky behaviors, such as substance abuse? | Adolescents under peer pressure are more likely to engage in risky behaviors like substance abuse compared to those who are not. |
Impact of Advertising on Consumer Preferences | Does advertising influence consumers’ preferences and purchasing decisions? | Consumers exposed to persuasive advertising tend to develop preferences for the advertised products and make purchasing decisions based on the ads. |
Effect of Teacher Feedback on Student Performance | How does the type of feedback provided by teachers affect students’ academic performance? | Students who receive specific and constructive feedback from teachers tend to demonstrate improved academic performance. |
In quantitative research, researchers aim to collect and analyze numerical data to answer specific research questions. A quantitative research question is designed to be measurable and testable, and it often involves examining the relationship between variables. The corresponding hypothesis statement predicts the expected outcome of the research based on previous knowledge or theories.
Effect of Exercise on Weight Loss | How does regular exercise impact weight loss in individuals? | Individuals who engage in regular exercise will experience greater weight loss. |
Relationship Between Sleep and Productivity | Is there a correlation between sleep duration and productivity levels? | Longer sleep durations are associated with higher levels of productivity. |
Impact of Smartphone Use on Academic Performance | How does smartphone use affect students’ academic performance? | Increased smartphone use leads to decreased academic performance in students. |
Influence of Social Support on Stress | How does social support mitigate stress levels in individuals? | Higher levels of social support result in lower stress levels among individuals. |
Effects of Advertising Frequency on Sales | Does the frequency of advertising exposure affect product sales? | Higher advertising frequency leads to increased product sales. |
Relationship Between Coffee Consumption and Alertness | Is there a relationship between coffee consumption and alertness levels? | Individuals who consume more coffee tend to experience higher levels of alertness. |
Impact of Study Time on Exam Scores | How does the amount of time spent studying affect exam scores? | Longer study hours are associated with improved exam scores. |
Effect of Age on Memory Recall | Does age have an impact on memory recall ability? | Older individuals exhibit lower memory recall compared to younger ones. |
Influence of Price on Consumer Preference | How does the price of a product influence consumers’ preferences? | Consumers are more likely to prefer products with lower prices. |
Relationship Between Screen Time and Sleep Quality | Is there a link between screen time and the quality of sleep? | Increased screen time before bed is linked to poorer sleep quality. |
Psychology is the scientific study of human behavior and mental processes. Psychology research questions delve into various aspects of human behavior, cognition, emotion, and more. These questions are designed to gain a deeper understanding of psychological phenomena. Hypothesis statements for psychology hypothesis research predict how certain factors or variables might influence human behavior or mental processes.
Impact of Mindfulness on Stress Reduction | How does practicing mindfulness meditation affect individuals’ stress levels? | Individuals who engage in mindfulness meditation experience reduced levels of stress. |
Relationship Between Parenting Style and Behavior | Is there a correlation between parenting styles and children’s behavior? | Authoritative parenting is associated with positive behavior outcomes in children compared to other styles. |
Effects of Music on Mood and Emotion | How does listening to different types of music influence individuals’ mood and emotional states? | Upbeat music genres are more likely to improve individuals’ mood and evoke positive emotions. |
Influence of Self-Efficacy on Achievement | How does individuals’ self-efficacy beliefs affect their academic and professional achievements? | Individuals with high self-efficacy tend to achieve greater success in both academic and professional domains. |
Impact of Color on Cognitive Performance | How does exposure to different colors affect cognitive performance and concentration? | Certain colors, like blue and green, enhance cognitive performance and attention compared to others. |
Relationship Between Personality and Leadership | Is there a link between personality traits and effective leadership skills? | Individuals with extroverted and conscientious personality traits tend to exhibit stronger leadership skills. |
Effects of Social Media on Body Image | How does frequent exposure to social media impact individuals’ body image perceptions? | Increased social media use contributes to negative body image perceptions and lowered self-esteem. |
Influence of Peer Pressure on Decision Making | How does peer pressure influence individuals’ decision-making processes? | Individuals under peer pressure are more likely to make decisions against their personal preferences. |
Impact of Childhood Trauma on Mental Health | Does childhood trauma have lasting effects on individuals’ mental health outcomes? | Individuals who experienced childhood trauma are more susceptible to long-term mental health issues. |
Relationship Between Empathy and Altruistic Behavior | Is there a connection between empathy levels and engaging in altruistic actions? | Individuals with higher empathy tend to engage in more frequent acts of altruism towards others. |
Testable research questions are formulated in a way that allows them to be tested through empirical observation or experimentation. These questions are often used in scientific and experimental research to investigate cause-and-effect relationships between variables. The corresponding hypothesis statements propose an expected outcome based on the variables being studied and the conditions of the experiment.
Effect of Vitamin C on Immune System | Can vitamin C supplementation enhance the immune system’s ability to fight off infections? | Individuals taking vitamin C supplements will experience fewer instances of infections. |
Relationship Between Study Methods and Grades | Is there a correlation between study methods and students’ academic grades? | Students who use active study methods will achieve higher grades compared to passive methods. |
Impact of Advertisement Placement on Sales | How does the placement of advertisements influence product sales in retail stores? | Advertisements placed near checkout counters lead to increased product sales. |
Influence of Sleep on Reaction Times | Does sleep duration affect individuals’ reaction times in cognitive tasks? | Individuals with adequate sleep will exhibit faster reaction times in cognitive tasks. |
Effects of Temperature on Productivity | How does room temperature impact employees’ productivity in an office environment? | Comfortable room temperatures enhance employees’ productivity compared to extreme temperatures. |
Relationship Between Exercise and Heart Health | Is there a link between regular exercise and improved heart health? | Individuals who engage in regular exercise have lower risks of heart-related health issues. |
Impact of Adjective Use on Persuasion | Can the use of positive adjectives enhance the persuasiveness of marketing messages? | Marketing messages incorporating positive adjectives lead to greater persuasion effects. |
Influence of Background Music on Creativity | How does background music affect individuals’ creativity levels during tasks? | Background music enhances individuals’ creativity during tasks requiring creative thinking. |
Relationship Between Diet and Blood Pressure | Is there a correlation between dietary habits and blood pressure levels? | Individuals following a low-sodium diet tend to have lower blood pressure readings. |
Effect of Leadership Style on Employee Morale | How does leadership style impact employee morale in a corporate setting? | Transformational leadership fosters higher employee morale compared to autocratic leadership. |
The hypothesis statement and research question statement are closely related but not the same. Both play crucial roles in research, but they serve distinct purposes.
Research question :.
Remember, both research questions and hypotheses play essential roles in guiding your research and framing the investigation’s purpose and expected outcomes.
Text prompt
10 Examples of Public speaking
20 Examples of Gas lighting
Background The Coronavirus Disease 2019 (COVID-19) pandemic and associated public health measures had an impact on alcohol use. Based on the literature of past crises (health, economic, etc.), it was hypothesized that the COVID-19 pandemic led to a polarization of drinking–that is, heavy drinkers increased their drinking, while light to moderate drinkers decreased their drinking and/or temporarily abstained. The aim of the current study was to test the respective hypothesis.
Methods Data from the Reducing Alcohol Related Harm Standard European Alcohol Survey for Lithuania were obtained for 2015 and 2020. Average daily consumption (in grams per day) was decomposed into deciles for each year, and compared pre-COVID to onset-of-COVID pandemic across the highest, second highest, and lowest deciles. A comparison of population-levels of mental health was conducted between pre-COVID and the onset-of-COVID.
Results The findings indicated that overall, there was higher consumption in 2015, M 2015 = 11.49 (SD = 8.23) vs. M 2020 = 10.71 (SD = 12.12), p < .00001. However the opposite was found in the highest decile M 2015 = 29.26 (SD = 5.44) vs. M 2020 = 39.23 (SD = 20.58), p = .0003. This reversal pattern was not observed in the second highest nor the lowest decile. There was a lower proportion of respondents indicating “bad” mental health pre- vs.post-COVID (3.4% vs. 6.5%).
Conclusion Although COVID was associated with nationwide declines in alcohol consumption, this was not the case for all segments of the population. In Lithuania, it appears that there was an increase in consumption among the heaviest drinkers, supporting the polarization hypothesis.
The authors have declared no competing interest.
The authors would like to acknowledge the National Institute on Alcohol Abuse and Alcoholism (NIAAA) (Award Number 1R01AA028224) of the National Institutes of Health for funding this research.
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
Ethics Statement All analyses received Centre for Addiction and Mental Health (CAMH) Research Ethics Board (REB) approval as per protocol: #050/2020.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
All data produced in the present study are available upon reasonable request to the authors
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Hypothesis testing example. You want to test whether there is a relationship between gender and height. Based on your knowledge of human physiology, you formulate a hypothesis that men are, on average, taller than women. To test this hypothesis, you restate it as: H 0: Men are, on average, not taller than women. H a: Men are, on average, taller ...
In statistics, hypothesis tests are used to test whether or not some hypothesis about a population parameter is true. To perform a hypothesis test in the real world, researchers will obtain a random sample from the population and perform a hypothesis test on the sample data, using a null and alternative hypothesis:. Null Hypothesis (H 0): The sample data occurs purely from chance.
If the engineer used the P -value approach to conduct his hypothesis test, he would determine the area under a tn - 1 = t24 curve and to the right of the test statistic t * = 1.22: In the output above, Minitab reports that the P -value is 0.117. Since the P -value, 0.117, is greater than α = 0.05, the engineer fails to reject the null hypothesis.
5 Steps of Significance Testing. Hypothesis testing involves five key steps, each critical to validating a research hypothesis using statistical methods: Formulate the Hypotheses: Write your research hypotheses as a null hypothesis (H 0) and an alternative hypothesis (H A ). Data Collection: Gather data specifically aimed at testing the ...
Example 3: Public Opinion About President Step 1. Determine the null and alternative hypotheses. Null hypothesis: There is no clear winning opinion on this issue; the proportions who would answer yes or no are each 0.50. Alternative hypothesis: Fewer than 0.50, or 50%, of the population would answer yes to this question.
Developing a hypothesis (with example) 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. Example: Research question.
The null hypothesis, denoted as H 0, is the hypothesis that the sample data occurs purely from chance. The alternative hypothesis, denoted as H 1 or H a, is the hypothesis that the sample data is influenced by some non-random cause. Hypothesis Tests. A hypothesis test consists of five steps: 1. State the hypotheses. State the null and ...
Test Statistic: z = x¯¯¯ −μo σ/ n−−√ z = x ¯ − μ o σ / n since it is calculated as part of the testing of the hypothesis. Definition 7.1.4 7.1. 4. p - value: probability that the test statistic will take on more extreme values than the observed test statistic, given that the null hypothesis is true.
Hypothesis testing is a crucial procedure to perform when you want to make inferences about a population using a random sample. These inferences include estimating population properties such as the mean, differences between means, proportions, and the relationships between variables. This post provides an overview of statistical hypothesis testing.
Hypothesis testing is a scientific method used for making a decision and drawing conclusions by using a statistical approach. It is used to suggest new ideas by testing theories to know whether or not the sample data supports research. A research hypothesis is a predictive statement that has to be tested using scientific methods that join an ...
Hypothesis testing is a technique that is used to verify whether the results of an experiment are statistically significant. It involves the setting up of a null hypothesis and an alternate hypothesis. There are three types of tests that can be conducted under hypothesis testing - z test, t test, and chi square test.
Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence.
Unit test. Significance tests give us a formal process for using sample data to evaluate the likelihood of some claim about a population value. Learn how to conduct significance tests and calculate p-values to see how likely a sample result is to occur by random chance. You'll also see how we use p-values to make conclusions about hypotheses.
ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults). Predictor variable. Outcome variable. Research question example. Paired t-test. Categorical. 1 predictor. Quantitative. groups come from the same population.
The hypothesis testing broadly involves the following steps, Step 1: Formulate the research hypothesis and the null hypothesis of the experiment. Step 2: Set the characteristics of the comparison distribution. Step3: Set the criterion for decision making, i.e., cut off sample score for the comparison to reject or retain the null hypothesis.
Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used ...
Hypothesis testing, then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
Selecting the sample size is important in hypothesis testing. The sample size chosen impacts the risk of Type I and Type 2 errors. The sample size also directly determines the confidence levels and the power of the test. The sample size formula can be resorted to arrive at the sample size. List of Topics. Hypothesis Testing Tutoring
Hypothesis Testing Examples and Solutions. Let's delve into some common examples of hypothesis testing and provide solutions or interpretations for each scenario. Example: Testing the Mean. Scenario: A coffee shop owner believes that the average waiting time for customers during peak hours is 5 minutes. To test this, the owner takes a random ...
Hypothesis Testing Examples. Before we get ahead and start understanding more details about hypothesis and hypothesis testing steps, lets take a look at some real-world examples of how to think about hypothesis and hypothesis testing when dealing with real-world problems: Customer churn: Customer churn is one of the most common problem one come across when starting to work with AI / machine ...
Standardized Test Statistics for Small Sample Hypothesis Tests Concerning a Single Population Mean ... For this reason the tests in the two examples in this section will be made following the critical value approach to hypothesis testing summarized at the end of Section 8.1, but after each one we will show how the \(p\)-value approach could ...
P-Value: The p-value is the level of marginal significance within a statistical hypothesis test representing the probability of the occurrence of a given event. The p-value is used as an ...
The hypothesis statement predicts a specific outcome, stating that there will be a positive impact on mental well-being for those who exercise regularly. The hypothesis guides the research process and provides a clear expectation for the study's results. 100 Research Question and Hypothesis Statement Examples
The aim of the current study was to test the respective hypothesis. Methods Data from the Reducing Alcohol Related Harm Standard European Alcohol Survey for Lithuania were obtained for 2015 and 2020. Average daily consumption (in grams per day) was decomposed into deciles for each year, and compared pre-COVID to onset-of-COVID pandemic across ...