how to write an alternative hypothesis example

9.1 Null and Alternative Hypotheses

The actual test begins by considering two hypotheses . They are called the null hypothesis and the alternative hypothesis . These hypotheses contain opposing viewpoints.

H 0 , the — null hypothesis: a statement of no difference between sample means or proportions or no difference between a sample mean or proportion and a population mean or proportion. In other words, the difference equals 0.

H a —, the alternative hypothesis: a claim about the population that is contradictory to H 0 and what we conclude when we reject H 0 .

Since the null and alternative hypotheses are contradictory, you must examine evidence to decide if you have enough evidence to reject the null hypothesis or not. The evidence is in the form of sample data.

After you have determined which hypothesis the sample supports, you make a decision. There are two options for a decision. They are reject H 0 if the sample information favors the alternative hypothesis or do not reject H 0 or decline to reject H 0 if the sample information is insufficient to reject the null hypothesis.

Mathematical Symbols Used in H 0 and H a :

H 0 always has a symbol with an equal in it. H a never has a symbol with an equal in it. The choice of symbol depends on the wording of the hypothesis test. However, be aware that many researchers use = in the null hypothesis, even with > or < as the symbol in the alternative hypothesis. This practice is acceptable because we only make the decision to reject or not reject the null hypothesis.

Example 9.1

H 0 : No more than 30 percent of the registered voters in Santa Clara County voted in the primary election. p ≤ 30 H a : More than 30 percent of the registered voters in Santa Clara County voted in the primary election. p > 30

A medical trial is conducted to test whether or not a new medicine reduces cholesterol by 25 percent. State the null and alternative hypotheses.

Example 9.2

We want to test whether the mean GPA of students in American colleges is different from 2.0 (out of 4.0). The null and alternative hypotheses are the following: H 0 : μ = 2.0 H a : μ ≠ 2.0

We want to test whether the mean height of eighth graders is 66 inches. State the null and alternative hypotheses. Fill in the correct symbol (=, ≠, ≥, <, ≤, >) for the null and alternative hypotheses.

• H 0 : μ __ 66
• H a : μ __ 66

Example 9.3

We want to test if college students take fewer than five years to graduate from college, on the average. The null and alternative hypotheses are the following: H 0 : μ ≥ 5 H a : μ < 5

We want to test if it takes fewer than 45 minutes to teach a lesson plan. State the null and alternative hypotheses. Fill in the correct symbol ( =, ≠, ≥, <, ≤, >) for the null and alternative hypotheses.

• H 0 : μ __ 45
• H a : μ __ 45

Example 9.4

An article on school standards stated that about half of all students in France, Germany, and Israel take advanced placement exams and a third of the students pass. The same article stated that 6.6 percent of U.S. students take advanced placement exams and 4.4 percent pass. Test if the percentage of U.S. students who take advanced placement exams is more than 6.6 percent. State the null and alternative hypotheses. H 0 : p ≤ 0.066 H a : p > 0.066

On a state driver’s test, about 40 percent pass the test on the first try. We want to test if more than 40 percent pass on the first try. Fill in the correct symbol (=, ≠, ≥, <, ≤, >) for the null and alternative hypotheses.

• H 0 : p __ 0.40
• H a : p __ 0.40

Collaborative Exercise

Bring to class a newspaper, some news magazines, and some internet articles. In groups, find articles from which your group can write null and alternative hypotheses. Discuss your hypotheses with the rest of the class.

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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

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See an 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.

Step 2. Do some preliminary research

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

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Prevent plagiarism. Run a free check.

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

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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

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• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

Prevent plagiarism, run a free check.

Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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Neag School of Education

Educational Research Basics by Del Siegle

Null and alternative hypotheses.

Converting research questions to hypothesis is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists between the groups (experiment study) and you have the alternative hypothesis. Write the statement such that a relationship does not exist or a difference does not exist and you have the null hypothesis. You can reverse the process if you have a hypothesis and wish to write a research question.

When you are comparing two groups, the groups are the independent variable. When you are testing whether something affects something else, the cause is the independent variable. The independent variable is the one you manipulate.

Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay. The first step is to ask yourself “Are there two or more groups being compared?” The answer is “Yes.” What are the groups? Teachers who are given higher pay and teachers who are given lower pay. The independent variable is teacher pay. The dependent variable (the outcome) is attitude towards school.

You could also approach is another way. “Is something causing something else?” The answer is “Yes.”  What is causing what? Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

By tradition, we try to disprove (reject) the null hypothesis. We can never prove a null hypothesis, because it is impossible to prove something does not exist. We can disprove something does not exist by finding an example of it. Therefore, in research we try to disprove the null hypothesis. When we do find that a relationship (or difference) exists then we reject the null and accept the alternative. If we do not find that a relationship (or difference) exists, we fail to reject the null hypothesis (and go with it). We never say we accept the null hypothesis because it is never possible to prove something does not exist. That is why we say that we failed to reject the null hypothesis, rather than we accepted it.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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10.1 - setting the hypotheses: examples.

A significance test examines whether the null hypothesis provides a plausible explanation of the data. The null hypothesis itself does not involve the data. It is a statement about a parameter (a numerical characteristic of the population). These population values might be proportions or means or differences between means or proportions or correlations or odds ratios or any other numerical summary of the population. The alternative hypothesis is typically the research hypothesis of interest. Here are some examples.

Example 10.2: Hypotheses with One Sample of One Categorical Variable Section  

About 10% of the human population is left-handed. Suppose a researcher at Penn State speculates that students in the College of Arts and Architecture are more likely to be left-handed than people found in the general population. We only have one sample since we will be comparing a population proportion based on a sample value to a known population value.

• Research Question : Are artists more likely to be left-handed than people found in the general population?
• Response Variable : Classification of the student as either right-handed or left-handed

State Null and Alternative Hypotheses

• Null Hypothesis : Students in the College of Arts and Architecture are no more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Art and Architecture = 10% or p = .10).
• Alternative Hypothesis : Students in the College of Arts and Architecture are more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Arts and Architecture > 10% or p > .10). This is a one-sided alternative hypothesis.

Example 10.3: Hypotheses with One Sample of One Measurement Variable Section  

A generic brand of the anti-histamine Diphenhydramine markets a capsule with a 50 milligram dose. The manufacturer is worried that the machine that fills the capsules has come out of calibration and is no longer creating capsules with the appropriate dosage.

• Research Question : Does the data suggest that the population mean dosage of this brand is different than 50 mg?
• Response Variable : dosage of the active ingredient found by a chemical assay.
• Null Hypothesis : On the average, the dosage sold under this brand is 50 mg (population mean dosage = 50 mg).
• Alternative Hypothesis : On the average, the dosage sold under this brand is not 50 mg (population mean dosage ≠ 50 mg). This is a two-sided alternative hypothesis.

Example 10.4: Hypotheses with Two Samples of One Categorical Variable Section  

Many people are starting to prefer vegetarian meals on a regular basis. Specifically, a researcher believes that females are more likely than males to eat vegetarian meals on a regular basis.

• Research Question : Does the data suggest that females are more likely than males to eat vegetarian meals on a regular basis?
• Response Variable : Classification of whether or not a person eats vegetarian meals on a regular basis
• Explanatory (Grouping) Variable: Sex
• Null Hypothesis : There is no sex effect regarding those who eat vegetarian meals on a regular basis (population percent of females who eat vegetarian meals on a regular basis = population percent of males who eat vegetarian meals on a regular basis or p females = p males ).
• Alternative Hypothesis : Females are more likely than males to eat vegetarian meals on a regular basis (population percent of females who eat vegetarian meals on a regular basis > population percent of males who eat vegetarian meals on a regular basis or p females > p males ). This is a one-sided alternative hypothesis.

Example 10.5: Hypotheses with Two Samples of One Measurement Variable Section  

Obesity is a major health problem today. Research is starting to show that people may be able to lose more weight on a low carbohydrate diet than on a low fat diet.

• Research Question : Does the data suggest that, on the average, people are able to lose more weight on a low carbohydrate diet than on a low fat diet?
• Response Variable : Weight loss (pounds)
• Explanatory (Grouping) Variable : Type of diet
• Null Hypothesis : There is no difference in the mean amount of weight loss when comparing a low carbohydrate diet with a low fat diet (population mean weight loss on a low carbohydrate diet = population mean weight loss on a low fat diet).
• Alternative Hypothesis : The mean weight loss should be greater for those on a low carbohydrate diet when compared with those on a low fat diet (population mean weight loss on a low carbohydrate diet > population mean weight loss on a low fat diet). This is a one-sided alternative hypothesis.

Example 10.6: Hypotheses about the relationship between Two Categorical Variables Section  

• Research Question : Do the odds of having a stroke increase if you inhale second hand smoke ? A case-control study of non-smoking stroke patients and controls of the same age and occupation are asked if someone in their household smokes.
• Variables : There are two different categorical variables (Stroke patient vs control and whether the subject lives in the same household as a smoker). Living with a smoker (or not) is the natural explanatory variable and having a stroke (or not) is the natural response variable in this situation.
• Null Hypothesis : There is no relationship between whether or not a person has a stroke and whether or not a person lives with a smoker (odds ratio between stroke and second-hand smoke situation is = 1).
• Alternative Hypothesis : There is a relationship between whether or not a person has a stroke and whether or not a person lives with a smoker (odds ratio between stroke and second-hand smoke situation is > 1). This is a one-tailed alternative.

This research question might also be addressed like example 11.4 by making the hypotheses about comparing the proportion of stroke patients that live with smokers to the proportion of controls that live with smokers.

Example 10.7: Hypotheses about the relationship between Two Measurement Variables Section  

• Research Question : A financial analyst believes there might be a positive association between the change in a stock's price and the amount of the stock purchased by non-management employees the previous day (stock trading by management being under "insider-trading" regulatory restrictions).
• Variables : Daily price change information (the response variable) and previous day stock purchases by non-management employees (explanatory variable). These are two different measurement variables.
• Null Hypothesis : The correlation between the daily stock price change (\$) and the daily stock purchases by non-management employees (\$) = 0.
• Alternative Hypothesis : The correlation between the daily stock price change (\$) and the daily stock purchases by non-management employees (\$) > 0. This is a one-sided alternative hypothesis.

Example 10.8: Hypotheses about comparing the relationship between Two Measurement Variables in Two Samples Section  

• Research Question : Is there a linear relationship between the amount of the bill (\$) at a restaurant and the tip (\$) that was left. Is the strength of this association different for family restaurants than for fine dining restaurants?
• Variables : There are two different measurement variables. The size of the tip would depend on the size of the bill so the amount of the bill would be the explanatory variable and the size of the tip would be the response variable.
• Null Hypothesis : The correlation between the amount of the bill (\$) at a restaurant and the tip (\$) that was left is the same at family restaurants as it is at fine dining restaurants.
• Alternative Hypothesis : The correlation between the amount of the bill (\$) at a restaurant and the tip (\$) that was left is the difference at family restaurants then it is at fine dining restaurants. This is a two-sided alternative hypothesis.

Alternative hypothesis

by Marco Taboga , PhD

In a statistical test, observed data is used to decide whether or not to reject a restriction on the data-generating probability distribution.

The assumption that the restriction is true is called null hypothesis , while the statement that the restriction is not true is called alternative hypothesis.

A correct specification of the alternative hypothesis is essential to decide between one-tailed and two-tailed tests.

Table of contents

Mathematical setting

Choice between one-tailed and two-tailed tests, the critical region, the interpretation of the rejection, the interpretation must be coherent with the alternative hypothesis.

• Power function

Accepting the alternative

More details, keep reading the glossary.

In order to fully understand the concept of alternative hypothesis, we need to remember the essential elements of a statistical inference problem:

we observe a sample drawn from an unknown probability distribution;

in principle, any valid probability distribution could have generated the sample;

however, we usually place some a priori restrictions on the set of possible data-generating distributions;

A couple of simple examples follow.

When we conduct a statistical test, we formulate a null hypothesis as a restriction on the statistical model.

The alternative hypothesis is

The alternative hypothesis is used to decide whether a test should be one-tailed or two-tailed.

The null hypothesis is rejected if the test statistic falls within a critical region that has been chosen by the statistician.

The critical region is a set of values that may comprise:

only the left tail of the distribution or only the right tail (one-tailed test);

both the left and the right tail (two-tailed test).

The choice of the critical region depends on the alternative hypothesis. Let us see why.

The interpretation is different depending on the tail of the distribution in which the test statistic falls.

The choice between a one-tailed or a two-tailed test needs to be done in such a way that the interpretation of a rejection is always coherent with the alternative hypothesis.

When we deal with the power function of a test, the term "alternative hypothesis" has a special meaning.

We conclude with a caveat about the interpretation of the outcome of a test of hypothesis.

The interpretation of a rejection of the null is controversial.

According to some statisticians, rejecting the null is equivalent to accepting the alternative.

However, others deem that rejecting the null does not necessarily imply accepting the alternative. In fact, it is possible to think of situations in which both hypotheses can be rejected. Let us see why.

According to the conceptual framework illustrated by the images above, there are three possibilities:

the null is true;

the alternative is true;

neither the null nor the alternative is true because the true data-generating distribution has been excluded from the statistical model (we say that the model is mis-specified).

If we are in case 3, accepting the alternative after a rejection of the null is an incorrect decision. Moreover, a second test in which the alternative becomes the new null may lead us to another rejection.

You can find more details about the alternative hypothesis in the lecture on Hypothesis testing .

Previous entry: Almost sure

Next entry: Binomial coefficient

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Educational resources and simple solutions for your research journey

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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Alternate Hypothesis in Statistics: What is it?

Statistics Definitions > Alternate Hypothesis

In order to understand what an alternate hypothesis (also called an alternative hypothesis ) is, you first need to understand what the null hypothesis means. The word hypothesis means a working statement. In statistics, we’re interested in proving whether a working statement (the null hypothesis) is true or false. Usually, these working statements are things that are expected to be true —some kind of known or fact or perhaps a historical value. The word “null” can be thought of as “no change”. With the null hypothesis, you get what you expect, from a historical point of view.

Need help writing a hypothesis? Check out our tutoring page!

The Alternate Hypothesis

In other cases, there might be a change in the amount of something. For example, let’s say a Gallup poll predicts an election will re-elect a president with a 5 percent majority. However, you, the researcher, has uncovered a secret grassroots campaign composed of hundreds of thousands of minorities who are going to vote the opposite way from expected.

• Null hypothesis : President re-elected with 5 percent majority
• Alternate hypothesis: President re-elected with 1-2 percent majority.

Although the outcome hasn’t changed (the President is still re-elected), the majority percentage has changed—which may be important to an electoral campaign.

The alternate hypothesis is usually what you will be testing in hypothesis testing. It’s a statement that you or another researcher) thinks is true and one that can ultimately lead you to reject the null hypothesis and replace it with the alternate hypothesis.

Alternate Hypothesis Examples

Example 1: It’s an accepted fact that ethanol boils at 173.1°F; you have a theory that ethanol actually has a different boiling point, of over 174°F. The accepted fact (“ethanol boils at 173.1°F”) is the null hypothesis ; your theory (“ethanol boils at temperatures of 174°F”) is the alternate hypothesis.

Example 2: A classroom full of students at a certain elementary school is performing at lower than average levels on standardized tests. The low test scores are thought to be due to poor teacher performance. However, you have a theory that the students are performing poorly because their classroom is not as well ventilated as the other classrooms in the school. The accepted theory (“low test scores are due to poor teacher performance”) is the null hypothesis ; your theory (“low test scores are due to inadequate ventilation in the classroom”) is the alternative hypothesis.

Next : How to State the Null Hypothesis in Statistics

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Gonick, L. (1993). The Cartoon Guide to Statistics . HarperPerennial. Kotz, S.; et al., eds. (2006), Encyclopedia of Statistical Sciences , Wiley.

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Statistics Made Easy

How to Write a Null Hypothesis (5 Examples)

A hypothesis test uses sample data to determine whether or not some claim about a population parameter is true.

Whenever we perform a hypothesis test, we always write a null hypothesis and an alternative hypothesis, which take the following forms:

H 0 (Null Hypothesis): Population parameter =,  ≤, ≥ some value

H A  (Alternative Hypothesis): Population parameter <, >, ≠ some value

Note that the null hypothesis always contains the equal sign .

We interpret the hypotheses as follows:

Null hypothesis: The sample data provides no evidence to support some claim being made by an individual.

Alternative hypothesis: The sample data  does provide sufficient evidence to support the claim being made by an individual.

For example, suppose it’s assumed that the average height of a certain species of plant is 20 inches tall. However, one botanist claims the true average height is greater than 20 inches.

To test this claim, she may go out and collect a random sample of plants. She can then use this sample data to perform a hypothesis test using the following two hypotheses:

H 0 : μ ≤ 20 (the true mean height of plants is equal to or even less than 20 inches)

H A : μ > 20 (the true mean height of plants is greater than 20 inches)

If the sample data gathered by the botanist shows that the mean height of this species of plants is significantly greater than 20 inches, she can reject the null hypothesis and conclude that the mean height is greater than 20 inches.

Read through the following examples to gain a better understanding of how to write a null hypothesis in different situations.

Example 1: Weight of Turtles

A biologist wants to test whether or not the true mean weight of a certain species of turtles is 300 pounds. To test this, he goes out and measures the weight of a random sample of 40 turtles.

Here is how to write the null and alternative hypotheses for this scenario:

H 0 : μ = 300 (the true mean weight is equal to 300 pounds)

H A : μ ≠ 300 (the true mean weight is not equal to 300 pounds)

Example 2: Height of Males

It’s assumed that the mean height of males in a certain city is 68 inches. However, an independent researcher believes the true mean height is greater than 68 inches. To test this, he goes out and collects the height of 50 males in the city.

H 0 : μ ≤ 68 (the true mean height is equal to or even less than 68 inches)

H A : μ > 68 (the true mean height is greater than 68 inches)

Example 3: Graduation Rates

A university states that 80% of all students graduate on time. However, an independent researcher believes that less than 80% of all students graduate on time. To test this, she collects data on the proportion of students who graduated on time last year at the university.

H 0 : p ≥ 0.80 (the true proportion of students who graduate on time is 80% or higher)

H A : μ < 0.80 (the true proportion of students who graduate on time is less than 80%)

Example 4: Burger Weights

A food researcher wants to test whether or not the true mean weight of a burger at a certain restaurant is 7 ounces. To test this, he goes out and measures the weight of a random sample of 20 burgers from this restaurant.

H 0 : μ = 7 (the true mean weight is equal to 7 ounces)

H A : μ ≠ 7 (the true mean weight is not equal to 7 ounces)

Example 5: Citizen Support

A politician claims that less than 30% of citizens in a certain town support a certain law. To test this, he goes out and surveys 200 citizens on whether or not they support the law.

H 0 : p ≥ .30 (the true proportion of citizens who support the law is greater than or equal to 30%)

H A : μ < 0.30 (the true proportion of citizens who support the law is less than 30%)

Additional Resources

Introduction to Hypothesis Testing Introduction to Confidence Intervals An Explanation of P-Values and Statistical Significance

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Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

2 Replies to “How to Write a Null Hypothesis (5 Examples)”

you are amazing, thank you so much

Say I am a botanist hypothesizing the average height of daisies is 20 inches, or not? Does T = (ave – 20 inches) / √ variance / (80 / 4)? … This assumes 40 real measures + 40 fake = 80 n, but that seems questionable. Please advise.

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Alternative Hypothesis

Alternative hypothesis defines there is a statistically important relationship between two variables. Whereas null hypothesis states there is no statistical relationship between the two variables. In statistics, we usually come across various kinds of hypotheses. A statistical hypothesis is supposed to be a working statement which is assumed to be logical with given data. It should be noticed that a hypothesis is neither considered true nor false.

The alternative hypothesis is a statement used in statistical inference experiment. It is contradictory to the null hypothesis and denoted by H a or H 1 . We can also say that it is simply an alternative to the null. In hypothesis testing, an alternative theory is a statement which a researcher is testing. This statement is true from the researcher’s point of view and ultimately proves to reject the null to replace it with an alternative assumption. In this hypothesis, the difference between two or more variables is predicted by the researchers, such that the pattern of data observed in the test is not due to chance.

To check the water quality of a river for one year, the researchers are doing the observation. As per the null hypothesis, there is no change in water quality in the first half of the year as compared to the second half. But in the alternative hypothesis, the quality of water is poor in the second half when observed.

Difference Between Null and Alternative Hypothesis

Basically, there are three types of the alternative hypothesis, they are;

Left-Tailed : Here, it is expected that the sample proportion (π) is less than a specified value which is denoted by π 0 , such that;

H 1 : π < π 0

Right-Tailed: It represents that the sample proportion (π) is greater than some value, denoted by π 0 .

H 1 : π > π 0

Two-Tailed: According to this hypothesis, the sample proportion (denoted by π) is not equal to a specific value which is represented by π 0 .

H 1 : π ≠ π 0

Note: The null hypothesis for all the three alternative hypotheses, would be H 1 : π = π 0 .

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Statistics By Jim

Making statistics intuitive

Null Hypothesis: Definition, Rejecting & Examples

By Jim Frost 6 Comments

What is a Null Hypothesis?

The null hypothesis in statistics states that there is no difference between groups or no relationship between variables. It is one of two mutually exclusive hypotheses about a population in a hypothesis test.

• Null Hypothesis H 0 : No effect exists in the population.
• Alternative Hypothesis H A : The effect exists in the population.

In every study or experiment, researchers assess an effect or relationship. This effect can be the effectiveness of a new drug, building material, or other intervention that has benefits. There is a benefit or connection that the researchers hope to identify. Unfortunately, no effect may exist. In statistics, we call this lack of an effect the null hypothesis. Researchers assume that this notion of no effect is correct until they have enough evidence to suggest otherwise, similar to how a trial presumes innocence.

In this context, the analysts don’t necessarily believe the null hypothesis is correct. In fact, they typically want to reject it because that leads to more exciting finds about an effect or relationship. The new vaccine works!

You can think of it as the default theory that requires sufficiently strong evidence to reject. Like a prosecutor, researchers must collect sufficient evidence to overturn the presumption of no effect. Investigators must work hard to set up a study and a data collection system to obtain evidence that can reject the null hypothesis.

Related post : What is an Effect in Statistics?

Null Hypothesis Examples

Null hypotheses start as research questions that the investigator rephrases as a statement indicating there is no effect or relationship.

After reading these examples, you might think they’re a bit boring and pointless. However, the key is to remember that the null hypothesis defines the condition that the researchers need to discredit before suggesting an effect exists.

Let’s see how you reject the null hypothesis and get to those more exciting findings!

When to Reject the Null Hypothesis

So, you want to reject the null hypothesis, but how and when can you do that? To start, you’ll need to perform a statistical test on your data. The following is an overview of performing a study that uses a hypothesis test.

The first step is to devise a research question and the appropriate null hypothesis. After that, the investigators need to formulate an experimental design and data collection procedures that will allow them to gather data that can answer the research question. Then they collect the data. For more information about designing a scientific study that uses statistics, read my post 5 Steps for Conducting Studies with Statistics .

After data collection is complete, statistics and hypothesis testing enter the picture. Hypothesis testing takes your sample data and evaluates how consistent they are with the null hypothesis. The p-value is a crucial part of the statistical results because it quantifies how strongly the sample data contradict the null hypothesis.

When the sample data provide sufficient evidence, you can reject the null hypothesis. In a hypothesis test, this process involves comparing the p-value to your significance level .

Rejecting the Null Hypothesis

Reject the null hypothesis when the p-value is less than or equal to your significance level. Your sample data favor the alternative hypothesis, which suggests that the effect exists in the population. For a mnemonic device, remember—when the p-value is low, the null must go!

When you can reject the null hypothesis, your results are statistically significant. Learn more about Statistical Significance: Definition & Meaning .

Failing to Reject the Null Hypothesis

Conversely, when the p-value is greater than your significance level, you fail to reject the null hypothesis. The sample data provides insufficient data to conclude that the effect exists in the population. When the p-value is high, the null must fly!

Note that failing to reject the null is not the same as proving it. For more information about the difference, read my post about Failing to Reject the Null .

That’s a very general look at the process. But I hope you can see how the path to more exciting findings depends on being able to rule out the less exciting null hypothesis that states there’s nothing to see here!

Let’s move on to learning how to write the null hypothesis for different types of effects, relationships, and tests.

Related posts : How Hypothesis Tests Work and Interpreting P-values

How to Write a Null Hypothesis

The null hypothesis varies by the type of statistic and hypothesis test. Remember that inferential statistics use samples to draw conclusions about populations. Consequently, when you write a null hypothesis, it must make a claim about the relevant population parameter . Further, that claim usually indicates that the effect does not exist in the population. Below are typical examples of writing a null hypothesis for various parameters and hypothesis tests.

Related posts : Descriptive vs. Inferential Statistics and Populations, Parameters, and Samples in Inferential Statistics

Group Means

T-tests and ANOVA assess the differences between group means. For these tests, the null hypothesis states that there is no difference between group means in the population. In other words, the experimental conditions that define the groups do not affect the mean outcome. Mu (µ) is the population parameter for the mean, and you’ll need to include it in the statement for this type of study.

For example, an experiment compares the mean bone density changes for a new osteoporosis medication. The control group does not receive the medicine, while the treatment group does. The null states that the mean bone density changes for the control and treatment groups are equal.

• Null Hypothesis H 0 : Group means are equal in the population: µ 1 = µ 2 , or µ 1 – µ 2 = 0
• Alternative Hypothesis H A : Group means are not equal in the population: µ 1 ≠ µ 2 , or µ 1 – µ 2 ≠ 0.

Group Proportions

Proportions tests assess the differences between group proportions. For these tests, the null hypothesis states that there is no difference between group proportions. Again, the experimental conditions did not affect the proportion of events in the groups. P is the population proportion parameter that you’ll need to include.

For example, a vaccine experiment compares the infection rate in the treatment group to the control group. The treatment group receives the vaccine, while the control group does not. The null states that the infection rates for the control and treatment groups are equal.

• Null Hypothesis H 0 : Group proportions are equal in the population: p 1 = p 2 .
• Alternative Hypothesis H A : Group proportions are not equal in the population: p 1 ≠ p 2 .

Correlation and Regression Coefficients

Some studies assess the relationship between two continuous variables rather than differences between groups.

In these studies, analysts often use either correlation or regression analysis . For these tests, the null states that there is no relationship between the variables. Specifically, it says that the correlation or regression coefficient is zero. As one variable increases, there is no tendency for the other variable to increase or decrease. Rho (ρ) is the population correlation parameter and beta (β) is the regression coefficient parameter.

For example, a study assesses the relationship between screen time and test performance. The null states that there is no correlation between this pair of variables. As screen time increases, test performance does not tend to increase or decrease.

• Null Hypothesis H 0 : The correlation in the population is zero: ρ = 0.
• Alternative Hypothesis H A : The correlation in the population is not zero: ρ ≠ 0.

For all these cases, the analysts define the hypotheses before the study. After collecting the data, they perform a hypothesis test to determine whether they can reject the null hypothesis.

The preceding examples are all for two-tailed hypothesis tests. To learn about one-tailed tests and how to write a null hypothesis for them, read my post One-Tailed vs. Two-Tailed Tests .

Related post : Understanding Correlation

Neyman, J; Pearson, E. S. (January 1, 1933).  On the Problem of the most Efficient Tests of Statistical Hypotheses .  Philosophical Transactions of the Royal Society A .  231  (694–706): 289–337.

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January 11, 2024 at 2:57 pm

Thanks for the reply.

January 10, 2024 at 1:23 pm

Hi Jim, In your comment you state that equivalence test null and alternate hypotheses are reversed. For hypothesis tests of data fits to a probability distribution, the null hypothesis is that the probability distribution fits the data. Is this correct?

January 10, 2024 at 2:15 pm

Those two separate things, equivalence testing and normality tests. But, yes, you’re correct for both.

Hypotheses are switched for equivalence testing. You need to “work” (i.e., collect a large sample of good quality data) to be able to reject the null that the groups are different to be able to conclude they’re the same.

With typical hypothesis tests, if you have low quality data and a low sample size, you’ll fail to reject the null that they’re the same, concluding they’re equivalent. But that’s more a statement about the low quality and small sample size than anything to do with the groups being equal.

So, equivalence testing make you work to obtain a finding that the groups are the same (at least within some amount you define as a trivial difference).

For normality testing, and other distribution tests, the null states that the data follow the distribution (normal or whatever). If you reject the null, you have sufficient evidence to conclude that your sample data don’t follow the probability distribution. That’s a rare case where you hope to fail to reject the null. And it suffers from the problem I describe above where you might fail to reject the null simply because you have a small sample size. In that case, you’d conclude the data follow the probability distribution but it’s more that you don’t have enough data for the test to register the deviation. In this scenario, if you had a larger sample size, you’d reject the null and conclude it doesn’t follow that distribution.

I don’t know of any equivalence testing type approach for distribution fit tests where you’d need to work to show the data follow a distribution, although I haven’t looked for one either!

February 20, 2022 at 9:26 pm

Is a null hypothesis regularly (always) stated in the negative? “there is no” or “does not”

February 23, 2022 at 9:21 pm

Typically, the null hypothesis includes an equal sign. The null hypothesis states that the population parameter equals a particular value. That value is usually one that represents no effect. In the case of a one-sided hypothesis test, the null still contains an equal sign but it’s “greater than or equal to” or “less than or equal to.” If you wanted to translate the null hypothesis from its native mathematical expression, you could use the expression “there is no effect.” But the mathematical form more specifically states what it’s testing.

It’s the alternative hypothesis that typically contains does not equal.

There are some exceptions. For example, in an equivalence test where the researchers want to show that two things are equal, the null hypothesis states that they’re not equal.

In short, the null hypothesis states the condition that the researchers hope to reject. They need to work hard to set up an experiment and data collection that’ll gather enough evidence to be able to reject the null condition.

February 15, 2022 at 9:32 am

Dear sir I always read your notes on Research methods.. Kindly tell is there any available Book on all these..wonderfull Urgent

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AI Generator

When doing a research action plan students in school would know that the first thing to do is to know your topic well enough. From expecting science projects to work based on your predictions and the results that may have been quite the opposite from how you depicted them. This also rings true in businesses. There is a term for that and it is often associated with the subject Science, but can also be associated with business . Scientific method  or a hypothesis.

What Is a Hypothesis?

A hypothesis is a scientific wild guess, a prediction in research . A wild guess, a say from someone without any known proof.  A hypothesis can also mean a scientific, educated guess that most scientists and researchers do before planning out or doing experiments to check if their guesses or their scientific ideas based on their topics are exact or correct.

Hypothesis Format

A well-structured hypothesis is crucial for guiding scientific research. Here’s a detailed format for writing a hypothesis, along with examples for each step:

1. Start with a Research Question

Before writing a hypothesis, begin with a clear and concise research question . This question identifies the focus of your study.

Example Research Question: Does the amount of daily exercise affect weight loss?

2. Identify the Variables

Identify the independent and dependent variables in your research question.

• Independent Variable: The variable you manipulate (e.g., amount of daily exercise).
• Dependent Variable: The variable you measure (e.g., weight loss).

3. Formulate the Hypothesis

Use the identified variables to create a testable statement . This statement should clearly express the expected relationship between the variables.

• If [independent variable], then [dependent variable].
• [Independent variable] will [effect] [dependent variable].

Directional vs. Non-Directional Hypothesis:

• Specifies the direction of the expected relationship.
• Does not specify the direction of the expected relationship, only that a relationship exists.

4. Example Hypotheses Using the Format

Research question: does caffeine affect cognitive performance, if-then statement:.

• Example: If individuals consume caffeine, then their cognitive performance will improve.

Direct Statement:

• Example: Caffeine consumption will improve cognitive performance.

Null Hypothesis (H0):

• Example: There is no significant effect of caffeine consumption on cognitive performance.

Alternative Hypothesis (H1):

• Example: There is a significant effect of caffeine consumption on cognitive performance.

Directional Hypothesis:

Non-directional hypothesis:.

• Example: There is a relationship between caffeine consumption and cognitive performance.

5. Refining the Hypothesis

Ensure that your hypothesis is specific, measurable, and testable. Avoid vague terms and focus on a single independent and dependent variable.

Hypothesis Examples in Research

A hypothesis is a statement that predicts the relationship between variables. It serves as a foundation for research by providing a clear focus and direction for experiments and data analysis . Here are examples of hypotheses from various fields of research:

Research Question:

Does sunlight exposure affect plant growth?

Hypotheses:

• Null Hypothesis (H0): There is no significant difference in plant growth between plants exposed to sunlight and those kept in the shade.
• Alternative Hypothesis (H1): Plants exposed to sunlight grow taller than those kept in the shade.
• Directional Hypothesis: Increased sunlight exposure will lead to increased plant growth.
• If-Then Statement: If plants are exposed to more sunlight, then they will grow taller.

2. Psychology

Does sleep duration affect memory retention?

• Null Hypothesis (H0): There is no significant difference in memory retention between individuals who sleep for 8 hours and those who sleep for 4 hours.
• Alternative Hypothesis (H1): Individuals who sleep for 8 hours will have better memory retention than those who sleep for 4 hours.
• Directional Hypothesis: Longer sleep duration will improve memory retention.
• If-Then Statement: If individuals sleep for 8 hours, then their memory retention will improve compared to those who sleep for 4 hours.

3. Education

Do interactive teaching methods improve student engagement?

• Null Hypothesis (H0): There is no significant difference in student engagement between interactive teaching methods and traditional lecture-based methods.
• Alternative Hypothesis (H1): Interactive teaching methods result in higher student engagement compared to traditional lecture-based methods.
• Directional Hypothesis: Interactive teaching methods will increase student engagement.
• If-Then Statement: If teachers use interactive teaching methods, then student engagement will increase.

4. Medicine

Does a new drug reduce blood pressure more effectively than the standard medication?

• Null Hypothesis (H0): There is no significant difference in blood pressure reduction between the new drug and the standard medication.
• Alternative Hypothesis (H1): The new drug reduces blood pressure more effectively than the standard medication.
• Directional Hypothesis: The new drug will reduce blood pressure more than the standard medication.
• If-Then Statement: If patients take the new drug, then their blood pressure will decrease more than if they take the standard medication.

5. Sociology

Does socioeconomic status affect access to higher education?

• Null Hypothesis (H0): There is no significant relationship between socioeconomic status and access to higher education.
• Alternative Hypothesis (H1): Higher socioeconomic status is associated with greater access to higher education.
• Directional Hypothesis: Individuals with higher socioeconomic status will have greater access to higher education.
• If-Then Statement: If individuals have a higher socioeconomic status, then they will have greater access to higher education.

Hypothesis Examples in Psychology

Psychology research often explores the relationships between various cognitive, behavioral, and emotional variables. Here are some well-structured hypothesis examples in psychology:

1. Sleep Duration and Memory Retention

• Non-Directional Hypothesis: There is a relationship between sleep duration and memory retention.

2. Exercise and Anxiety Levels

Does regular exercise reduce anxiety levels?

• Null Hypothesis (H0): There is no significant difference in anxiety levels between individuals who exercise regularly and those who do not.
• Alternative Hypothesis (H1): Individuals who exercise regularly will have lower anxiety levels than those who do not.
• Directional Hypothesis: Regular exercise will decrease anxiety levels.
• Non-Directional Hypothesis: There is a relationship between regular exercise and anxiety levels.
• If-Then Statement: If individuals exercise regularly, then their anxiety levels will decrease.

3. Social Media Usage and Self-Esteem

Does social media usage affect self-esteem in teenagers?

• Null Hypothesis (H0): There is no significant relationship between social media usage and self-esteem in teenagers.
• Alternative Hypothesis (H1): High social media usage is associated with lower self-esteem in teenagers.
• Directional Hypothesis: Increased social media usage will decrease self-esteem in teenagers.
• Non-Directional Hypothesis: There is a relationship between social media usage and self-esteem in teenagers.
• If-Then Statement: If teenagers spend more time on social media, then their self-esteem will decrease.

4. Cognitive Behavioral Therapy (CBT) and Depression

Is Cognitive Behavioral Therapy (CBT) effective in reducing symptoms of depression?

• Null Hypothesis (H0): There is no significant difference in depression symptoms between individuals who undergo CBT and those who do not.
• Alternative Hypothesis (H1): Individuals who undergo CBT will experience a greater reduction in depression symptoms than those who do not.
• Directional Hypothesis: CBT will reduce symptoms of depression.
• Non-Directional Hypothesis: There is a relationship between undergoing CBT and reduction in depression symptoms.
• If-Then Statement: If individuals undergo CBT, then their symptoms of depression will decrease.

5. Parental Involvement and Academic Achievement

Does parental involvement influence academic achievement in children?

• Null Hypothesis (H0): There is no significant relationship between parental involvement and academic achievement in children.
• Alternative Hypothesis (H1): Higher levels of parental involvement are associated with higher academic achievement in children.
• Directional Hypothesis: Increased parental involvement will improve academic achievement in children.
• Non-Directional Hypothesis: There is a relationship between parental involvement and academic achievement in children.
• If-Then Statement: If parents are more involved in their children’s education, then their children will achieve higher academic success.

Hypothesis Examples in Science

Scientific research often involves creating hypotheses to test the relationships between variables. Here are some well-structured hypothesis examples from various fields of science:

1. Biology: Sunlight and Plant Growth

• Non-Directional Hypothesis: There is a relationship between sunlight exposure and plant growth.

2. Chemistry: Temperature and Reaction Rate

Does temperature affect the rate of a chemical reaction?

• Null Hypothesis (H0): There is no significant difference in the reaction rate of a chemical reaction at different temperatures.
• Alternative Hypothesis (H1): Increasing the temperature will increase the reaction rate.
• Directional Hypothesis: Higher temperatures will increase the reaction rate.
• Non-Directional Hypothesis: There is a relationship between temperature and the reaction rate.
• If-Then Statement: If the temperature of a reaction increases, then the reaction rate will increase.

3. Physics: Mass and Free Fall Speed

Does the mass of an object affect its speed when falling?

• Null Hypothesis (H0): There is no significant difference in the falling speed of objects with different masses.
• Alternative Hypothesis (H1): Objects with greater mass fall faster than those with lesser mass.
• Directional Hypothesis: Heavier objects will fall faster than lighter objects.
• Non-Directional Hypothesis: There is a relationship between the mass of an object and its falling speed.
• If-Then Statement: If an object’s mass increases, then its falling speed will increase.

4. Environmental Science: Fertilizers and Water Quality

Do chemical fertilizers affect water quality in nearby lakes?

• Null Hypothesis (H0): There is no significant effect of chemical fertilizers on the water quality of nearby lakes.
• Alternative Hypothesis (H1): Chemical fertilizers negatively affect the water quality of nearby lakes.
• Directional Hypothesis: The use of chemical fertilizers will decrease the water quality of nearby lakes.
• Non-Directional Hypothesis: There is a relationship between the use of chemical fertilizers and the water quality of nearby lakes.
• If-Then Statement: If chemical fertilizers are used, then the water quality in nearby lakes will decrease.

5. Earth Science: Soil Composition and Erosion Rate

Does soil composition affect the rate of erosion?

• Null Hypothesis (H0): There is no significant difference in the erosion rate of soils with different compositions.
• Alternative Hypothesis (H1): Soil composition affects the rate of erosion.
• Directional Hypothesis: Soils with higher clay content will erode more slowly than sandy soils.
• Non-Directional Hypothesis: There is a relationship between soil composition and the rate of erosion.
• If-Then Statement: If soil has a higher clay content, then its erosion rate will be lower compared to sandy soil.

Hypothesis Examples in Biology

In biology, hypotheses are used to explore relationships and effects within biological systems. Here are some well-structured hypothesis examples in various areas of biology:

1. Photosynthesis and Light Intensity

How does light intensity affect the rate of photosynthesis in plants?

• Null Hypothesis (H0): Light intensity has no significant effect on the rate of photosynthesis in plants.
• Alternative Hypothesis (H1): Light intensity significantly affects the rate of photosynthesis in plants.
• Directional Hypothesis: Increased light intensity will increase the rate of photosynthesis in plants.
• Non-Directional Hypothesis: There is a relationship between light intensity and the rate of photosynthesis in plants.
• If-Then Statement: If light intensity increases, then the rate of photosynthesis in plants will increase.

2. Temperature and Enzyme Activity

How does temperature affect the activity of the enzyme amylase?

• Null Hypothesis (H0): Temperature has no significant effect on the activity of the enzyme amylase.
• Alternative Hypothesis (H1): Temperature significantly affects the activity of the enzyme amylase.
• Directional Hypothesis: Increasing the temperature will increase the activity of the enzyme amylase up to an optimal point, after which activity will decrease.
• Non-Directional Hypothesis: There is a relationship between temperature and the activity of the enzyme amylase.
• If-Then Statement: If the temperature increases, then the activity of the enzyme amylase will increase up to an optimal temperature, after which it will decrease.

3. Nutrient Availability and Plant Growth

Does the availability of nutrients in soil affect the growth of plants?

• Null Hypothesis (H0): Nutrient availability has no significant effect on the growth of plants.
• Alternative Hypothesis (H1): Nutrient availability significantly affects the growth of plants.
• Directional Hypothesis: Increased nutrient availability will enhance plant growth.
• Non-Directional Hypothesis: There is a relationship between nutrient availability and plant growth.
• If-Then Statement: If nutrient availability in the soil increases, then the growth of plants will be enhanced.

4. Genetic Variation and Disease Resistance

Does genetic variation in a population affect its resistance to diseases?

• Null Hypothesis (H0): Genetic variation has no significant effect on disease resistance in a population.
• Alternative Hypothesis (H1): Genetic variation significantly affects disease resistance in a population.
• Directional Hypothesis: Populations with greater genetic variation will have higher resistance to diseases.
• Non-Directional Hypothesis: There is a relationship between genetic variation and disease resistance in a population.
• If-Then Statement: If a population has greater genetic variation, then its resistance to diseases will be higher.

5. Water pH and Aquatic Life Health

Does the pH level of water affect the health of aquatic life?

• Null Hypothesis (H0): The pH level of water has no significant effect on the health of aquatic life.
• Alternative Hypothesis (H1): The pH level of water significantly affects the health of aquatic life.
• Directional Hypothesis: Extreme pH levels (both high and low) will negatively affect the health of aquatic life.
• Non-Directional Hypothesis: There is a relationship between the pH level of water and the health of aquatic life.
• If-Then Statement: If the pH level of water is too high or too low, then the health of aquatic life will be negatively affected.

Hypothesis Examples in Sociology

In sociology, hypotheses are used to explore and explain social phenomena, behaviors, and relationships within societies. Here are some well-structured hypothesis examples in various areas of sociology:

1. Education and Social Mobility

Does access to higher education affect social mobility?

• Null Hypothesis (H0): Access to higher education has no significant effect on social mobility.
• Alternative Hypothesis (H1): Access to higher education significantly affects social mobility.
• Directional Hypothesis: Increased access to higher education will improve social mobility.
• Non-Directional Hypothesis: There is a relationship between access to higher education and social mobility.
• If-Then Statement: If individuals have increased access to higher education, then their social mobility will improve.

2. Income Inequality and Crime Rates

Does income inequality influence crime rates in urban areas?

• Null Hypothesis (H0): Income inequality has no significant effect on crime rates in urban areas.
• Alternative Hypothesis (H1): Income inequality significantly affects crime rates in urban areas.
• Directional Hypothesis: Higher income inequality will lead to higher crime rates in urban areas.
• Non-Directional Hypothesis: There is a relationship between income inequality and crime rates in urban areas.
• If-Then Statement: If income inequality increases, then crime rates in urban areas will increase.

3. Social Media Use and Social Interaction

Does the use of social media affect face-to-face social interactions among teenagers?

• Null Hypothesis (H0): The use of social media has no significant effect on face-to-face social interactions among teenagers.
• Alternative Hypothesis (H1): The use of social media significantly affects face-to-face social interactions among teenagers.
• Directional Hypothesis: Increased use of social media will decrease face-to-face social interactions among teenagers.
• Non-Directional Hypothesis: There is a relationship between the use of social media and face-to-face social interactions among teenagers.
• If-Then Statement: If teenagers use social media more frequently, then their face-to-face social interactions will decrease.

4. Gender Roles and Career Choices

Do traditional gender roles influence career choices among young adults?

• Null Hypothesis (H0): Traditional gender roles have no significant effect on career choices among young adults.
• Alternative Hypothesis (H1): Traditional gender roles significantly affect career choices among young adults.
• Directional Hypothesis: Adherence to traditional gender roles will limit career choices among young adults.
• Non-Directional Hypothesis: There is a relationship between traditional gender roles and career choices among young adults.
• If-Then Statement: If young adults adhere to traditional gender roles, then their career choices will be limited.

5. Cultural Diversity and Workplace Productivity

Does cultural diversity in the workplace affect productivity levels?

• Null Hypothesis (H0): Cultural diversity in the workplace has no significant effect on productivity levels.
• Alternative Hypothesis (H1): Cultural diversity in the workplace significantly affects productivity levels.
• Directional Hypothesis: Increased cultural diversity will improve productivity levels in the workplace.
• Non-Directional Hypothesis: There is a relationship between cultural diversity in the workplace and productivity levels.
• If-Then Statement: If the workplace has increased cultural diversity, then productivity levels will improve.

More Hypothesis Samples & Examples in PDF

1. research hypothesis.

2. Education Hypothesis

3. Basic Hypothesis

4. Hypothesis Statement Template

5. Hypothesis in PDF

6. Hypothesis Format

7. Hypothesis Examples

8. Simple Hypothesis

Types of Hypothesis

A hypothesis is a statement that can be tested and is often used in scientific research to propose a relationship between two or more variables. Understanding the different types of hypotheses is essential for conducting effective research. Below are the main types of hypotheses:

1. Null Hypothesis (H0)

The null hypothesis states that there is no relationship between the variables being studied. It assumes that any observed effect is due to chance. Researchers often aim to disprove the null hypothesis.

Example: There is no significant difference in test scores between students who study with music and those who study in silence.

2. Alternative Hypothesis (H1 or Ha)

The alternative hypothesis suggests that there is a relationship between the variables being studied. It is what researchers seek to prove.

Example: Students who study with music have higher test scores than those who study in silence.

3. Simple Hypothesis

A simple hypothesis predicts a relationship between a single independent variable and a single dependent variable.

Example: Increasing the amount of sunlight will increase the growth rate of plants.

4. Complex Hypothesis

A complex hypothesis predicts a relationship involving two or more independent variables and/or two or more dependent variables.

Example: Increasing sunlight and water will increase the growth rate and height of plants.

5. Directional Hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. It suggests whether the relationship is positive or negative.

Example: Students who study for more hours will score higher on exams.

6. Non-Directional Hypothesis

A non-directional hypothesis does not specify the direction of the relationship. It only states that a relationship exists.

Example: There is a difference in test scores between students who study with music and those who study in silence.

7. Statistical Hypothesis

A statistical hypothesis involves quantitative data and can be tested using statistical methods. It often includes both null and alternative hypotheses.

Example: The mean test scores of students who study with music are significantly different from those who study in silence.

8. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between variables. It suggests that one variable causes a change in another.

Example: Smoking causes lung cancer.

9. Associative Hypothesis

An associative hypothesis suggests that variables are related but does not imply causation.

Example: There is an association between physical activity levels and body weight.

10. Research Hypothesis

A research hypothesis is a broad statement that serves as the foundation for the research study. It is often the same as the alternative hypothesis.

Example: Implementing a new teaching strategy will improve student engagement and performance.

How To Use Hypothesis for Research?

A hypothesis is a critical component of the research process, providing a clear direction for the study and forming the basis for drawing conclusions. Here’s a step-by-step guide on how to use a hypothesis in research:

1. Identify the Research Problem

Before formulating a hypothesis, clearly define the research problem or question. This step involves understanding what you aim to investigate and why it is significant.

Example: You want to study the impact of sleep on academic performance among college students.

2. Review Existing Literature

Conduct a thorough review of existing literature to understand what is already known about the topic. This helps in identifying gaps in knowledge and forming a basis for your hypothesis.

Example: Previous studies suggest a positive correlation between sleep duration and academic performance but lack specific data on college students.

Based on the research problem and literature review, formulate a clear and testable hypothesis. Ensure it is specific and relates directly to the variables being studied.

Types of Hypotheses:

• Null Hypothesis (H0): There is no significant relationship between sleep duration and academic performance among college students.
• Alternative Hypothesis (H1): There is a significant relationship between sleep duration and academic performance among college students.

4. Define Variables

Clearly define the independent and dependent variables involved in the hypothesis.

• Independent Variable: Sleep duration
• Dependent Variable: Academic performance (e.g., GPA)

5. Design the Study

Choose an appropriate research design to test the hypothesis. This could be experimental, correlational, or observational, depending on the nature of your research question.

Example: Conduct a correlational study to examine the relationship between sleep duration and GPA among college students.

6. Collect Data

Gather data through surveys, experiments, or secondary data sources. Ensure the data collection methods are reliable and valid to accurately test the hypothesis.

Example: Use a questionnaire to collect data on students’ sleep duration and their GPAs.

7. Analyze the Data

Use appropriate statistical methods to analyze the data. This step involves testing the hypothesis to determine whether to accept or reject the null hypothesis.

Example: Perform a Pearson correlation analysis to examine the relationship between sleep duration and GPA.

8. Interpret the Results

Interpret the results of the statistical analysis. Determine if the data supports the alternative hypothesis or if the null hypothesis cannot be rejected.

Example: If the analysis shows a significant positive correlation, you can reject the null hypothesis and accept the alternative hypothesis that sleep duration is related to academic performance.

9. Draw Conclusions

Draw conclusions based on the results of the hypothesis testing. Discuss the implications of the findings and how they contribute to the existing body of knowledge.

Example: Conclude that longer sleep duration is associated with higher GPA among college students and discuss potential implications for student health and academic policies.

10. Report and Share Findings

Write a detailed report or research paper presenting the hypothesis, methodology, results, and conclusions. Share your findings with the academic community or relevant stakeholders.

Example: Publish the study in a peer-reviewed journal or present it at an academic conference.

How to Write a Hypothesis?

Writing a hypothesis is a crucial step in the scientific method. A well-constructed hypothesis guides your research, helping you design experiments and analyze results. Here’s a step-by-step guide on how to write an effective hypothesis:

1. Understand the Research Question

Start by clearly understanding the research question or problem you want to address. This helps in formulating a focused hypothesis.

Example: How does sunlight exposure affect plant growth?

2. Conduct Preliminary Research

Review existing literature related to your research question. This helps in understanding what is already known and identifying gaps in knowledge.

Example: Studies show that plants generally grow better with more sunlight, but the optimal amount varies.

3. Identify Variables

Determine the independent and dependent variables for your study.

• Independent Variable: The factor you manipulate (e.g., sunlight exposure).
• Dependent Variable: The factor you measure (e.g., plant growth).

4. Formulate a Simple Hypothesis

A simple hypothesis involves one independent and one dependent variable. Clearly state the expected relationship between these variables.

Example: Increasing sunlight exposure will increase plant growth.

5. Choose the Type of Hypothesis

Decide whether your hypothesis will be null or alternative, directional or non-directional.

• Null Hypothesis (H0): There is no relationship between the variables.
• Alternative Hypothesis (H1): There is a relationship between the variables.
• Directional Hypothesis: Specifies the direction of the relationship.
• Non-Directional Hypothesis: Does not specify the direction.

Example of Directional Hypothesis: Plants exposed to more sunlight will grow taller than those exposed to less sunlight.

6. Ensure Testability

Make sure your hypothesis can be tested through experiments or observations. It should be measurable and falsifiable.

Example: Plants will be grown under different levels of sunlight, and their growth will be measured over time.

7. Write the Hypothesis

Write your hypothesis in a clear, concise, and specific manner. It should include the variables and the expected relationship between them.

Example: If plants are exposed to increased sunlight, then they will grow taller compared to plants that receive less sunlight.

8. Refine the Hypothesis

Ensure that your hypothesis is specific and narrow enough to be testable but broad enough to cover the scope of your research.

Example: If tomato plants are exposed to 8 hours of sunlight per day, then they will grow taller and produce more fruit compared to tomato plants exposed to 4 hours of sunlight per day.

How Do You Formulate a Hypothesis?

To formulate a hypothesis, identify the research question, review existing literature, define variables, and create a testable statement predicting the relationship between the variables.

What Is the Difference Between Null and Alternative Hypotheses?

The null hypothesis (H0) states there is no effect or relationship, while the alternative hypothesis (H1) proposes that there is an effect or relationship.

Why Is a Hypothesis Important in Research?

A hypothesis provides a clear focus for the study, guiding the research design, data collection, and analysis, ultimately helping to draw meaningful conclusions.

Can a Hypothesis Be Proven True?

A hypothesis cannot be proven true; it can only be supported or refuted through experimentation and analysis. Even if supported, it remains open to further testing.

What Makes a Good Hypothesis?

A good hypothesis is clear, concise, specific, testable, and based on existing knowledge. It should predict a relationship between variables that can be measured.

How Is a Hypothesis Tested?

A hypothesis is tested through experiments or observations, collecting and analyzing data to determine if the results support or refute the hypothesis.

What Are the Types of Hypotheses?

Types of hypotheses include null, alternative, simple, complex, directional, non-directional, statistical, causal, and associative.

What Is a Directional Hypothesis?

A directional hypothesis specifies the expected direction of the relationship between variables, indicating whether the effect will be positive or negative.

What Is a Non-Directional Hypothesis?

A non-directional hypothesis states that a relationship exists between variables but does not specify the direction of the relationship.

How Do You Refine a Hypothesis?

Refine a hypothesis by ensuring it is specific, measurable, and testable. Remove any vague terms and focus on a single independent and dependent variable.

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