example of non directional alternative hypothesis

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.

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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.

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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.

Related Articles

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Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Thematic Analysis: A Step by Step Guide

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Hypotheses; directional and non-directional, what is the difference between an experimental and an alternative hypothesis.

Nothing much! If the study is a laboratory experiment then we can call the hypothesis “an experimental hypothesis”, where we make a prediction about how the IV causes an effect on the DV. If we have a non-experimental design, i.e. we are not able to manipulate the IV as in a natural or quasi-experiment , or if some other research method has been used, then we call it an “alternativehypothesis”, alternative to the null.

Directional hypothesis: A directional (or one tailed hypothesis) states which way you think the results are going to go, for example in an experimental study we might say…”Participants who have been deprived of sleep for 24 hours will have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived”; the hypothesis compares the two groups/conditions and states which one will ….have more/less, be quicker/slower, etc.

If we had a correlational study, the directional hypothesis would state whether we expect a positive or a negative correlation, we are stating how the two variables will be related to each other, e.g. there will be a positive correlation between the number of stressful life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”. The directional hypothesis can also state a negative correlation, e.g. the higher the number of face-book friends, the lower the life satisfaction score “

Non-directional hypothesis: A non-directional (or two tailed hypothesis) simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc. Using our example above we would say “There will be a difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.”

When the study is correlational, we simply state that variables will be correlated but do not state whether the relationship will be positive or negative, e.g. there will be a significant correlation between variable A and variable B.

Null hypothesis The null hypothesis states that the alternative or experimental hypothesis is NOT the case, if your experimental hypothesis was directional you would say…

Participants who have been deprived of sleep for 24 hours will NOT have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived and any difference that does arise will be due to chance alone.

or with a directional correlational hypothesis….

There will NOT be a positive correlation between the number of stress life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”

With a non-directional or  two tailed hypothesis…

There will be NO difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.

or for a correlational …

there will be NO correlation between variable A and variable B.

When it comes to conducting an inferential stats test, if you have a directional hypothesis , you must do a one tailed test to find out whether your observed value is significant. If you have a non-directional hypothesis , you must do a two tailed test .

Exam Techniques/Advice

• Remember, a decent hypothesis will contain two variables, in the case of an experimental hypothesis there will be an IV and a DV; in a correlational hypothesis there will be two co-variables
• both variables need to be fully operationalised to score the marks, that is you need to be very clear and specific about what you mean by your IV and your DV; if someone wanted to repeat your study, they should be able to look at your hypothesis and know exactly what to change between the two groups/conditions and exactly what to measure (including any units/explanation of rating scales etc, e.g. “where 1 is low and 7 is high”)
• double check the question, did it ask for a directional or non-directional hypothesis?
• if you were asked for a null hypothesis, make sure you always include the phrase “and any difference/correlation (is your study experimental or correlational?) that does arise will be due to chance alone”

Practice Questions:

• Mr Faraz wants to compare the levels of attendance between his psychology group and those of Mr Simon, who teaches a different psychology group. Which of the following is a suitable directional (one tailed) hypothesis for Mr Faraz’s investigation?

A There will be a difference in the levels of attendance between the two psychology groups.

B Students’ level of attendance will be higher in Mr Faraz’s group than Mr Simon’s group.

C Any difference in the levels of attendance between the two psychology groups is due to chance.

D The level of attendance of the students will depend upon who is teaching the groups.

2. Tracy works for the local council. The council is thinking about reducing the number of people it employs to pick up litter from the street. Tracy has been asked to carry out a study to see if having the streets cleaned at less regular intervals will affect the amount of litter the public will drop. She studies a street to compare how much litter is dropped at two different times, once when it has just been cleaned and once after it has not been cleaned for a month.

Write a fully operationalised non-directional (two-tailed) hypothesis for Tracy’s study. (2)

3. Jamila is conducting a practical investigation to look at gender differences in carrying out visuo-spatial tasks. She decides to give males and females a jigsaw puzzle and will time them to see who completes it the fastest. She uses a random sample of pupils from a local school to get her participants.

(a) Write a fully operationalised directional (one tailed) hypothesis for Jamila’s study. (2) (b) Outline one strength and one weakness of the random sampling method. You may refer to Jamila’s use of this type of sampling in your answer. (4)

4. Which of the following is a non-directional (two tailed) hypothesis?

A There is a difference in driving ability with men being better drivers than women

B Women are better at concentrating on more than one thing at a time than men

C Women spend more time doing the cooking and cleaning than men

D There is a difference in the number of men and women who participate in sports

Revision Activity

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Quizizz link for teachers: https://quizizz.com/admin/quiz/5bf03f51add785001bc5a09e

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Directional and non-directional hypothesis: A Comprehensive Guide

Karolina Konopka

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In the world of research and statistical analysis, hypotheses play a crucial role in formulating and testing scientific claims. Understanding the differences between directional and non-directional hypothesis is essential for designing sound experiments and drawing accurate conclusions. Whether you’re a student, researcher, or simply curious about the foundations of hypothesis testing, this guide will equip you with the knowledge and tools to navigate this fundamental aspect of scientific inquiry.

Understanding Directional Hypothesis

Understanding directional hypotheses is crucial for conducting hypothesis-driven research, as they guide the selection of appropriate statistical tests and aid in the interpretation of results. By incorporating directional hypotheses, researchers can make more precise predictions, contribute to scientific knowledge, and advance their fields of study.

Definition of directional hypothesis

Directional hypotheses, also known as one-tailed hypotheses, are statements in research that make specific predictions about the direction of a relationship or difference between variables. Unlike non-directional hypotheses, which simply state that there is a relationship or difference without specifying its direction, directional hypotheses provide a focused and precise expectation.

A directional hypothesis predicts either a positive or negative relationship between variables or predicts that one group will perform better than another. It asserts a specific direction of effect or outcome. For example, a directional hypothesis could state that “increased exposure to sunlight will lead to an improvement in mood” or “participants who receive the experimental treatment will exhibit higher levels of cognitive performance compared to the control group.”

Directional hypotheses are formulated based on existing theory, prior research, or logical reasoning, and they guide the researcher’s expectations and analysis. They allow for more targeted predictions and enable researchers to test specific hypotheses using appropriate statistical tests.

The role of directional hypothesis in research

Directional hypotheses also play a significant role in research surveys. Let’s explore their role specifically in the context of survey research:

• Objective-driven surveys : Directional hypotheses help align survey research with specific objectives. By formulating directional hypotheses, researchers can focus on gathering data that directly addresses the predicted relationship or difference between variables of interest.
• Question design and measurement : Directional hypotheses guide the design of survey question types and the selection of appropriate measurement scales. They ensure that the questions are tailored to capture the specific aspects related to the predicted direction, enabling researchers to obtain more targeted and relevant data from survey respondents.
• Data analysis and interpretation : Directional hypotheses assist in data analysis by directing researchers towards appropriate statistical tests and methods. Researchers can analyze the survey data to specifically test the predicted relationship or difference, enhancing the accuracy and reliability of their findings. The results can then be interpreted within the context of the directional hypothesis, providing more meaningful insights.
• Practical implications and decision-making : Directional hypotheses in surveys often have practical implications. When the predicted relationship or difference is confirmed, it informs decision-making processes, program development, or interventions. The survey findings based on directional hypotheses can guide organizations, policymakers, or practitioners in making informed choices to achieve desired outcomes.
• Replication and further research : Directional hypotheses in survey research contribute to the replication and extension of studies. Researchers can replicate the survey with different populations or contexts to assess the generalizability of the predicted relationships. Furthermore, if the directional hypothesis is supported, it encourages further research to explore underlying mechanisms or boundary conditions.

By incorporating directional hypotheses in survey research, researchers can align their objectives, design effective surveys, conduct focused data analysis, and derive practical insights. They provide a framework for organizing survey research and contribute to the accumulation of knowledge in the field.

Examples of research questions for directional hypothesis

Here are some examples of research questions that lend themselves to directional hypotheses:

• Does increased daily exercise lead to a decrease in body weight among sedentary adults?
• Is there a positive relationship between study hours and academic performance among college students?
• Does exposure to violent video games result in an increase in aggressive behavior among adolescents?
• Does the implementation of a mindfulness-based intervention lead to a reduction in stress levels among working professionals?
• Is there a difference in customer satisfaction between Product A and Product B, with Product A expected to have higher satisfaction ratings?
• Does the use of social media influence self-esteem levels, with higher social media usage associated with lower self-esteem?
• Is there a negative relationship between job satisfaction and employee turnover, indicating that lower job satisfaction leads to higher turnover rates?
• Does the administration of a specific medication result in a decrease in symptoms among individuals with a particular medical condition?
• Does increased access to early childhood education lead to improved cognitive development in preschool-aged children?
• Is there a difference in purchase intention between advertisements with celebrity endorsements and advertisements without, with celebrity endorsements expected to have a higher impact?

These research questions generate specific predictions about the direction of the relationship or difference between variables and can be tested using appropriate research methods and statistical analyses.

Definition of non-directional hypothesis

Non-directional hypotheses, also known as two-tailed hypotheses, are statements in research that indicate the presence of a relationship or difference between variables without specifying the direction of the effect. Instead of making predictions about the specific direction of the relationship or difference, non-directional hypotheses simply state that there is an association or distinction between the variables of interest.

Non-directional hypotheses are often used when there is no prior theoretical basis or clear expectation about the direction of the relationship. They leave the possibility open for either a positive or negative relationship, or for both groups to differ in some way without specifying which group will perform better or worse.

Advantages and utility of non-directional hypothesis

Non-directional hypotheses in survey s offer several advantages and utilities, providing flexibility and comprehensive analysis of survey data. Here are some of the key advantages and utilities of using non-directional hypotheses in surveys:

• Exploration of Relationships : Non-directional hypotheses allow researchers to explore and examine relationships between variables without assuming a specific direction. This is particularly useful in surveys where the relationship between variables may not be well-known or there may be conflicting evidence regarding the direction of the effect.
• Flexibility in Question Design : With non-directional hypotheses, survey questions can be designed to measure the relationship between variables without being biased towards a particular outcome. This flexibility allows researchers to collect data and analyze the results more objectively.
• Open to Unexpected Findings : Non-directional hypotheses enable researchers to be open to unexpected or surprising findings in survey data. By not committing to a specific direction of the effect, researchers can identify and explore relationships that may not have been initially anticipated, leading to new insights and discoveries.
• Comprehensive Analysis : Non-directional hypotheses promote comprehensive analysis of survey data by considering the possibility of an effect in either direction. Researchers can assess the magnitude and significance of relationships without limiting their analysis to only one possible outcome.
• S tatistical Validity : Non-directional hypotheses in surveys allow for the use of two-tailed statistical tests, which provide a more conservative and robust assessment of significance. Two-tailed tests consider both positive and negative deviations from the null hypothesis, ensuring accurate and reliable statistical analysis of survey data.
• Exploratory Research : Non-directional hypotheses are particularly useful in exploratory research, where the goal is to gather initial insights and generate hypotheses. Surveys with non-directional hypotheses can help researchers explore various relationships and identify patterns that can guide further research or hypothesis development.

It is worth noting that the choice between directional and non-directional hypotheses in surveys depends on the research objectives, existing knowledge, and the specific variables being investigated. Researchers should carefully consider the advantages and limitations of each approach and select the one that aligns best with their research goals and survey design.

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• Null and Alternative Hypotheses | Definitions & Examples

Null & Alternative Hypotheses | Definitions, Templates & Examples

Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis ( H 0 ): There’s no effect in the population .
• Alternative hypothesis ( H a or H 1 ) : There’s an effect in the population.

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”:

• The null hypothesis ( H 0 ) answers “No, there’s no effect in the population.”
• The alternative hypothesis ( H a ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample. It’s critical for your research to write strong hypotheses .

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

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The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept . Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect,” “no difference,” or “no relationship.” When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis ( H a ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect,” “a difference,” or “a relationship.” When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes < or >). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question.
• They both make claims about the population.
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

General template sentences

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis ( H 0 ): Independent variable does not affect dependent variable.
• Alternative hypothesis ( H a ): Independent variable affects dependent variable.

Test-specific template sentences

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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.

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.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

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

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

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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Null and Alternative Hypotheses | Definitions & Examples

Published on 5 October 2022 by Shaun Turney . Revised on 6 December 2022.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis (H 0 ): There’s no effect in the population .
• Alternative hypothesis (H A ): There’s an effect in the population.

The effect is usually the effect of the independent variable on the dependent variable .

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, differences between null and alternative hypotheses, how to write null and alternative hypotheses, frequently asked questions about null and alternative hypotheses.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”, the null hypothesis (H 0 ) answers “No, there’s no effect in the population.” On the other hand, the alternative hypothesis (H A ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample.

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept. Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect”, “no difference”, or “no relationship”. When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis (H A ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect”, “a difference”, or “a relationship”. When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes > or <). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question
• They both make claims about the population
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis (H 0 ): Independent variable does not affect dependent variable .
• Alternative hypothesis (H A ): Independent variable affects dependent variable .

Test-specific

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

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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Directional Hypothesis: Definition and 10 Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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A directional hypothesis refers to a type of hypothesis used in statistical testing that predicts a particular direction of the expected relationship between two variables.

In simpler terms, a directional hypothesis is an educated, specific guess about the direction of an outcome—whether an increase, decrease, or a proclaimed difference in variable sets.

For example, in a study investigating the effects of sleep deprivation on cognitive performance, a directional hypothesis might state that as sleep deprivation (Independent Variable) increases, cognitive performance (Dependent Variable) decreases (Killgore, 2010). Such a hypothesis offers a clear, directional relationship whereby a specific increase or decrease is anticipated.

Global warming provides another notable example of a directional hypothesis. A researcher might hypothesize that as carbon dioxide (CO2) levels increase, global temperatures also increase (Thompson, 2010). In this instance, the hypothesis clearly articulates an upward trend for both variables. 

In any given circumstance, it’s imperative that a directional hypothesis is grounded on solid evidence. For instance, the CO2 and global temperature relationship is based on substantial scientific evidence, and not on a random guess or mere speculation (Florides & Christodoulides, 2009).

Directional vs Non-Directional vs Null Hypotheses

A directional hypothesis is generally contrasted to a non-directional hypothesis. Here’s how they compare:

• Directional hypothesis: A directional hypothesis provides a perspective of the expected relationship between variables, predicting the direction of that relationship (either positive, negative, or a specific difference). 
• Non-directional hypothesis: A non-directional hypothesis denotes the possibility of a relationship between two variables ( the independent and dependent variables ), although this hypothesis does not venture a prediction as to the direction of this relationship (Ali & Bhaskar, 2016). For example, a non-directional hypothesis might state that there exists a relationship between a person’s diet (independent variable) and their mood (dependent variable), without indicating whether improvement in diet enhances mood positively or negatively. Overall, the choice between a directional or non-directional hypothesis depends on the known or anticipated link between the variables under consideration in research studies.

Another very important type of hypothesis that we need to know about is a null hypothesis :

• Null hypothesis : The null hypothesis stands as a universality—the hypothesis that there is no observed effect in the population under study, meaning there is no association between variables (or that the differences are down to chance). For instance, a null hypothesis could be constructed around the idea that changing diet (independent variable) has no discernible effect on a person’s mood (dependent variable) (Yan & Su, 2016). This proposition is the one that we aim to disprove in an experiment.

While directional and non-directional hypotheses involve some integrated expectations about the outcomes (either distinct direction or a vague relationship), a null hypothesis operates on the premise of negating such relationships or effects.

The null hypotheses is typically proposed to be negated or disproved by statistical tests, paving way for the acceptance of an alternate hypothesis (either directional or non-directional).

Directional Hypothesis Examples

1. exercise and heart health.

Research suggests that as regular physical exercise (independent variable) increases, the risk of heart disease (dependent variable) decreases (Jakicic, Davis, Rogers, King, Marcus, Helsel, Rickman, Wahed, Belle, 2016). In this example, a directional hypothesis anticipates that the more individuals maintain routine workouts, the lesser would be their odds of developing heart-related disorders. This assumption is based on the underlying fact that routine exercise can help reduce harmful cholesterol levels, regulate blood pressure, and bring about overall health benefits. Thus, a direction – a decrease in heart disease – is expected in relation with an increase in exercise. 

2. Screen Time and Sleep Quality

Another classic instance of a directional hypothesis can be seen in the relationship between the independent variable, screen time (especially before bed), and the dependent variable, sleep quality. This hypothesis predicts that as screen time before bed increases, sleep quality decreases (Chang, Aeschbach, Duffy, Czeisler, 2015). The reasoning behind this hypothesis is the disruptive effect of artificial light (especially blue light from screens) on melatonin production, a hormone needed to regulate sleep. As individuals spend more time exposed to screens before bed, it is predictably hypothesized that their sleep quality worsens. 

3. Job Satisfaction and Employee Turnover

A typical scenario in organizational behavior research posits that as job satisfaction (independent variable) increases, the rate of employee turnover (dependent variable) decreases (Cheng, Jiang, & Riley, 2017). This directional hypothesis emphasizes that an increased level of job satisfaction would lead to a reduced rate of employees leaving the company. The theoretical basis for this hypothesis is that satisfied employees often tend to be more committed to the organization and are less likely to seek employment elsewhere, thus reducing turnover rates.

4. Healthy Eating and Body Weight

Healthy eating, as the independent variable, is commonly thought to influence body weight, the dependent variable, in a positive way. For example, the hypothesis might state that as consumption of healthy foods increases, an individual’s body weight decreases (Framson, Kristal, Schenk, Littman, Zeliadt, & Benitez, 2009). This projection is based on the premise that healthier foods, such as fruits and vegetables, are generally lower in calories than junk food, assisting in weight management.

5. Sun Exposure and Skin Health

The association between sun exposure (independent variable) and skin health (dependent variable) allows for a definitive hypothesis declaring that as sun exposure increases, the risk of skin damage or skin cancer increases (Whiteman, Whiteman, & Green, 2001). The premise aligns with the understanding that overexposure to the sun’s ultraviolet rays can deteriorate skin health, leading to conditions like sunburn or, in extreme cases, skin cancer.

6. Study Hours and Academic Performance

A regularly assessed relationship in academia suggests that as the number of study hours (independent variable) rises, so too does academic performance (dependent variable) (Nonis, Hudson, Logan, Ford, 2013). The hypothesis proposes a positive correlation , with an increase in study time expected to contribute to enhanced academic outcomes.

7. Screen Time and Eye Strain

It’s commonly hypothesized that as screen time (independent variable) increases, the likelihood of experiencing eye strain (dependent variable) also increases (Sheppard & Wolffsohn, 2018). This is based on the idea that prolonged engagement with digital screens—computers, tablets, or mobile phones—can cause discomfort or fatigue in the eyes, attributing to symptoms of eye strain.

8. Physical Activity and Stress Levels

In the sphere of mental health, it’s often proposed that as physical activity (independent variable) increases, levels of stress (dependent variable) decrease (Stonerock, Hoffman, Smith, Blumenthal, 2015). Regular exercise is known to stimulate the production of endorphins, the body’s natural mood elevators, helping to alleviate stress.

9. Water Consumption and Kidney Health

A common health-related hypothesis might predict that as water consumption (independent variable) increases, the risk of kidney stones (dependent variable) decreases (Curhan, Willett, Knight, & Stampfer, 2004). Here, an increase in water intake is inferred to reduce the risk of kidney stones by diluting the substances that lead to stone formation.

10. Traffic Noise and Sleep Quality

In urban planning research, it’s often supposed that as traffic noise (independent variable) increases, sleep quality (dependent variable) decreases (Muzet, 2007). Increased noise levels, particularly during the night, can result in sleep disruptions, thus, leading to poor sleep quality.

11. Sugar Consumption and Dental Health

In the field of dental health, an example might be stating as one’s sugar consumption (independent variable) increases, dental health (dependent variable) decreases (Sheiham, & James, 2014). This stems from the fact that sugar is a major factor in tooth decay, and increased consumption of sugary foods or drinks leads to a decline in dental health due to the high likelihood of cavities.

See 15 More Examples of Hypotheses Here

A directional hypothesis plays a critical role in research, paving the way for specific predicted outcomes based on the relationship between two variables. These hypotheses clearly illuminate the expected direction—the increase or decrease—of an effect. From predicting the impacts of healthy eating on body weight to forecasting the influence of screen time on sleep quality, directional hypotheses allow for targeted and strategic examination of phenomena. In essence, directional hypotheses provide the crucial path for inquiry, shaping the trajectory of research studies and ultimately aiding in the generation of insightful, relevant findings.

Ali, S., & Bhaskar, S. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60 (9), 662-669. doi: https://doi.org/10.4103%2F0019-5049.190623  

Chang, A. M., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015). Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proceeding of the National Academy of Sciences, 112 (4), 1232-1237. doi: https://doi.org/10.1073/pnas.1418490112  

Cheng, G. H. L., Jiang, D., & Riley, J. H. (2017). Organizational commitment and intrinsic motivation of regular and contractual primary school teachers in China. New Psychology, 19 (3), 316-326. Doi: https://doi.org/10.4103%2F2249-4863.184631  

Curhan, G. C., Willett, W. C., Knight, E. L., & Stampfer, M. J. (2004). Dietary factors and the risk of incident kidney stones in younger women: Nurses’ Health Study II. Archives of Internal Medicine, 164 (8), 885–891.

Florides, G. A., & Christodoulides, P. (2009). Global warming and carbon dioxide through sciences. Environment international , 35 (2), 390-401. doi: https://doi.org/10.1016/j.envint.2008.07.007

Framson, C., Kristal, A. R., Schenk, J. M., Littman, A. J., Zeliadt, S., & Benitez, D. (2009). Development and validation of the mindful eating questionnaire. Journal of the American Dietetic Association, 109 (8), 1439-1444. doi: https://doi.org/10.1016/j.jada.2009.05.006  

Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., … & Belle, S. H. (2016). Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. JAMA, 316 (11), 1161-1171.

Khan, S., & Iqbal, N. (2013). Study of the relationship between study habits and academic achievement of students: A case of SPSS model. Higher Education Studies, 3 (1), 14-26.

Killgore, W. D. (2010). Effects of sleep deprivation on cognition. Progress in brain research , 185 , 105-129. doi: https://doi.org/10.1016/B978-0-444-53702-7.00007-5  

Marczinski, C. A., & Fillmore, M. T. (2014). Dissociative antagonistic effects of caffeine on alcohol-induced impairment of behavioral control. Experimental and Clinical Psychopharmacology, 22 (4), 298–311. doi: https://psycnet.apa.org/doi/10.1037/1064-1297.11.3.228  

Muzet, A. (2007). Environmental Noise, Sleep and Health. Sleep Medicine Reviews, 11 (2), 135-142. doi: https://doi.org/10.1016/j.smrv.2006.09.001  

Nonis, S. A., Hudson, G. I., Logan, L. B., & Ford, C. W. (2013). Influence of perceived control over time on college students’ stress and stress-related outcomes. Research in Higher Education, 54 (5), 536-552. doi: https://doi.org/10.1023/A:1018753706925  

Sheiham, A., & James, W. P. (2014). A new understanding of the relationship between sugars, dental caries and fluoride use: implications for limits on sugars consumption. Public health nutrition, 17 (10), 2176-2184. Doi: https://doi.org/10.1017/S136898001400113X  

Sheppard, A. L., & Wolffsohn, J. S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ open ophthalmology , 3 (1), e000146. doi: http://dx.doi.org/10.1136/bmjophth-2018-000146

Stonerock, G. L., Hoffman, B. M., Smith, P. J., & Blumenthal, J. A. (2015). Exercise as Treatment for Anxiety: Systematic Review and Analysis. Annals of Behavioral Medicine, 49 (4), 542–556. doi: https://doi.org/10.1007/s12160-014-9685-9  

Thompson, L. G. (2010). Climate change: The evidence and our options. The Behavior Analyst , 33 , 153-170. Doi: https://doi.org/10.1007/BF03392211  

Whiteman, D. C., Whiteman, C. A., & Green, A. C. (2001). Childhood sun exposure as a risk factor for melanoma: a systematic review of epidemiologic studies. Cancer Causes & Control, 12 (1), 69-82. doi: https://doi.org/10.1023/A:1008980919928

Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing . New Jersey: World Scientific.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 19 Top Cognitive Psychology Theories (Explained)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 119 Bloom’s Taxonomy Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ All 6 Levels of Understanding (on Bloom’s Taxonomy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Self-Actualization Examples (Maslow's Hierarchy)

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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.

Statistics Made Easy

What is an Alternative Hypothesis in Statistics?

Often in statistics we want to test whether or not some assumption is true about a population parameter .

For example, we might assume that the mean weight of a certain population of turtle is 300 pounds.

To determine if this assumption is true, we’ll go out and collect a sample of turtles and weigh each of them. Using this sample data, we’ll conduct a hypothesis test .

The first step in a hypothesis test is to define the  null and  alternative hypotheses .

These two hypotheses need to be mutually exclusive, so if one is true then the other must be false.

These two hypotheses are defined as follows:

Null hypothesis (H 0 ): The sample data is consistent with the prevailing belief about the population parameter.

Alternative hypothesis (H A ): The sample data suggests that the assumption made in the null hypothesis is not true. In other words, there is some non-random cause influencing the data.

Types of Alternative Hypotheses

There are two types of alternative hypotheses:

A  one-tailed hypothesis involves making a “greater than” or “less than ” statement. For example, suppose we assume the mean height of a male in the U.S. is greater than or equal to 70 inches.

The null and alternative hypotheses in this case would be:

• Null hypothesis: µ ≥ 70 inches
• Alternative hypothesis: µ < 70 inches

A  two-tailed hypothesis involves making an “equal to” or “not equal to” statement. For example, suppose we assume the mean height of a male in the U.S. is equal to 70 inches.

• Null hypothesis: µ = 70 inches
• Alternative hypothesis: µ ≠ 70 inches

Note: The “equal” sign is always included in the null hypothesis, whether it is =, ≥, or ≤.

Examples of Alternative Hypotheses

The following examples illustrate how to define the null and alternative hypotheses for different research problems.

Example 1: A biologist wants to test if the mean weight of a certain population of turtle is different from the widely-accepted mean weight of 300 pounds.

The null and alternative hypothesis for this research study would be:

• Null hypothesis: µ = 300 pounds
• Alternative hypothesis: µ ≠ 300 pounds

If we reject the null hypothesis, this means we have sufficient evidence from the sample data to say that the true mean weight of this population of turtles is different from 300 pounds.

Example 2: An engineer wants to test whether a new battery can produce higher mean watts than the current industry standard of 50 watts.

• Null hypothesis: µ ≤ 50 watts
• Alternative hypothesis: µ > 50 watts

If we reject the null hypothesis, this means we have sufficient evidence from the sample data to say that the true mean watts produced by the new battery is greater than the current industry standard of 50 watts.

Example 3: A botanist wants to know if a new gardening method produces less waste than the standard gardening method that produces 20 pounds of waste.

• Null hypothesis: µ ≥ 20 pounds
• Alternative hypothesis: µ < 20 pounds

If we reject the null hypothesis, this means we have sufficient evidence from the sample data to say that the true mean weight produced by this new gardening method is less than 20 pounds.

When to Reject the Null Hypothesis

Whenever we conduct a hypothesis test, we use sample data to calculate a test-statistic and a corresponding p-value.

If the p-value is less than some significance level (common choices are 0.10, 0.05, and 0.01), then we reject the null hypothesis.

This means we have sufficient evidence from the sample data to say that the assumption made by the null hypothesis is not true.

If the p-value is  not less than some significance level, then we fail to reject the null hypothesis.

This means our sample data did not provide us with evidence that the assumption made by the null hypothesis was not true.

Additional Resource:   An Explanation of P-Values and Statistical Significance

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Directional vs. Non-Directional Hypothesis in Research

In the world of research and statistical analysis, formulating hypotheses is a crucial step in the scientific process. Hypotheses guide researchers in making predictions and testing relationships between variables. When it comes to hypotheses, there are two main types: directional and non-directional.

Table of Contents

Directional Hypothesis

A directional hypothesis, also known as a one-tailed hypothesis, is formulated with a specific predicted direction of the relationship between variables. It indicates an expectation of the relationship being either positive or negative.

Example of Directional Hypothesis

Advantages of directional hypothesis, non-directional hypothesis.

Non-directional hypotheses are often used when there is insufficient prior knowledge or theoretical basis to predict the direction of the relationship. It allows for a more exploratory approach, where the researcher is open to discovering the nature of the relationship through data analysis .

Example of Non-Directional Hypothesis

Read More: Population vs Sample | Examples

Advantages of Non-Directional Hypothesis:

Difference between directional and non-directional hypotheses.

Choosing Between Directional and Non-Directional Hypotheses: The choice between a directional and non-directional hypothesis depends on the research question, existing knowledge, and theoretical background. Here are a few considerations for selecting the appropriate type of hypothesis:

Directional vs. Non-Directional Hypothesis

Directional hypotheses offer specific predictions about the expected direction of the relationship, whereas non-directional hypotheses allow for more exploratory investigations without preconceived notions of the direction.

Remember, hypotheses serve as a roadmap for research, and regardless of their type, they play a crucial role in scientific inquiry and the pursuit of knowledge.

Other articles

Related posts, alternative hypothesis: types and examples, causal research: examples, benefits, and practical tips, 7 types of observational studies | examples, writing an introduction for a research paper: a guide (with examples), clinical research design: elements and importance, dependent variable in research: examples, what is an independent variable, how to write a high-quality conference paper, what are scholarly sources and where can you find them, how to write a conclusion for research paper | examples, leave a reply cancel reply.

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Non-Directional Hypothesis

A non-directional hypothesis is a two-tailed hypothesis that does not predict the direction of the difference or relationship (e.g. girls and boys are different in terms of helpfulness).

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Directional vs Non-Directional Hypothesis – Collect Feedback More Effectively 

To conduct a perfect survey, you should know the basics of good research . That’s why in Startquestion we would like to share with you our knowledge about basic terms connected to online surveys and feedback gathering . Knowing the basis you can create surveys and conduct research in more effective ways and thanks to this get meaningful feedback from your customers, employees, and users. That’s enough for the introduction – let’s get to work. This time we will tell you about the hypothesis .

What is a Hypothesis?

A Hypothesis can be described as a theoretical statement built upon some evidence so that it can be tested as if it is true or false. In other words, a hypothesis is a speculation or an idea, based on insufficient evidence that allows it further analysis and experimentation.  

The purpose of a hypothetical statement is to work like a prediction based on studied research and to provide some estimated results before it ha happens in a real position. There can be more than one hypothesis statement involved in a research study, where you need to question and explore different aspects of a proposed research topic. Before putting your research into directional vs non-directional hypotheses, let’s have some basic knowledge.

Most often, a hypothesis describes a relation between two or more variables. It includes:

An Independent variable – One that is controlled by the researcher

Dependent Variable – The variable that the researcher observes in association with the Independent variable.

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How to write an effective Hypothesis?

To write an effective hypothesis follow these essential steps.

• Inquire a Question

The very first step in writing an effective hypothesis is raising a question. Outline the research question very carefully keeping your research purpose in mind. Build it in a precise and targeted way. Here you must be clear about the research question vs hypothesis. A research question is the very beginning point of writing an effective hypothesis.

Do Literature Review

Once you are done with constructing your research question, you can start the literature review. A literature review is a collection of preliminary research studies done on the same or relevant topics. There is a diversified range of literature reviews. The most common ones are academic journals but it is not confined to that. It can be anything including your research, data collection, and observation.

At this point, you can build a conceptual framework. It can be defined as a visual representation of the estimated relationship between two variables subjected to research.

Frame an Answer

After a collection of literature reviews, you can find ways how to answer the question. Expect this stage as a point where you will be able to make a stand upon what you believe might have the exact outcome of your research. You must formulate this answer statement clearly and concisely.

Build a Hypothesis

At this point, you can firmly build your hypothesis. By now, you knew the answer to your question so make a hypothesis that includes:

• Applicable Variables                     
• Particular Group being Studied (Who/What)
• Probable Outcome of the Experiment

Remember, your hypothesis is a calculated assumption, it has to be constructed as a sentence, not a question. This is where research question vs hypothesis starts making sense.

Refine a Hypothesis

Make necessary amendments to the constructed hypothesis keeping in mind that it has to be targeted and provable. Moreover, you might encounter certain circumstances where you will be studying the difference between one or more groups. It can be correlational research. In such instances, you must have to testify the relationships that you believe you will find in the subject variables and through this research.

Build Null Hypothesis

Certain research studies require some statistical investigation to perform a data collection. Whenever applying any scientific method to construct a hypothesis, you must have adequate knowledge of the Null Hypothesis and an Alternative hypothesis.

Null Hypothesis: 

A null Hypothesis denotes that there is no statistical relationship between the subject variables. It is applicable for a single group of variables or two groups of variables. A Null Hypothesis is denoted as an H0. This is the type of hypothesis that the researcher tries to invalidate. Some of the examples of null hypotheses are:

–        Hyperactivity is not associated with eating sugar.

–        All roses have an equal amount of petals.

–        A person’s preference for a dress is not linked to its color.

Alternative Hypothesis: 

An alternative hypothesis is a statement that is simply inverse or opposite of the null hypothesis and denoted as H1. Simply saying, it is an alternative statement for the null hypothesis. The same examples will go this way as an alternative hypothesis:

–        Hyperactivity is associated with eating sugar.

–        All roses do not have an equal amount of petals.

–        A person’s preference for a dress is linked to its color.

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Types of Hypothesis

Apart from null and alternative hypotheses, research hypotheses can be categorized into different types. Let’s have a look at them:

Simple Hypothesis:

This type of hypothesis is used to state a relationship between a particular independent variable and only a dependent variable.

Complex Hypothesis:

A statement that states the relationship between two or more independent variables and two or more dependent variables, is termed a complex hypothesis.

Associative and Causal Hypothesis:

This type of hypothesis involves predicting that there is a point of interdependency between two variables. It says that any kind of change in one variable will cause a change in the other one.  Similarly, a casual hypothesis says that a change in the dependent variable is due to some variations in the independent variable.

Directional vs non-directional hypothesis

Directional hypothesis:.

A hypothesis that is built upon a certain directional relationship between two variables and constructed upon an already existing theory, is called a directional hypothesis. To understand more about what is directional hypothesis here is an example, Girls perform better than boys (‘better than’ shows the direction predicted)

Non-directional Hypothesis:

It involves an open-ended non-directional hypothesis that predicts that the independent variable will influence the dependent variable; however, the nature or direction of a relationship between two subject variables is not defined or clear.

For Example, there will be a difference in the performance of girls & boys (Not defining what kind of difference)

As a professional, we suggest you apply a non-directional alternative hypothesis when you are not sure of the direction of the relationship. Maybe you’re observing potential gender differences on some psychological test, but you don’t know whether men or women would have the higher ratio. Normally, this would say that you are lacking practical knowledge about the proposed variables. A directional test should be more common for tests. 

Author: Ula Kamburov-Niepewna

Updated: 18 November 2022

Qualitative Survey Questions with Some Examples

Understanding your customers' experiences and perceptions is crucial for enhancing your products and services. While quantitative research provides numerical insights, qualitative survey questions dive deeper, exploring the "why" behind customer behaviors.

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Directional vs Non-Directional Hypothesis: Key Difference

In statistics, a directional hypothesis, also known as a one-tailed hypothesis, is a type of hypothesis that predicts the direction of the relationship between variables or the direction of the difference between groups.

The introduction of a directional hypothesis in a research study provides an overview of the specific prediction being made about the relationship between variables or the difference between groups. It sets the stage for the research question and outlines the expected direction of the findings. The introduction typically includes the following elements:

Research Context: Begin by introducing the general topic or research area that the study is focused on. Provide background information and highlight the significance of the research question.

Research Question: Clearly state the specific research question that the study aims to answer. This question should be directly related to the variables being investigated.

Previous Research: Summarize relevant literature or previous studies that have explored similar or related topics. This helps establish the existing knowledge base and provides a rationale for the hypothesis.

Hypothesis Statement: Present the directional hypothesis clearly and concisely. State the predicted relationship between variables or the expected difference between groups. For example, if studying the impact of a new teaching method on student performance, a directional hypothesis could be, “Students who receive the new teaching method will demonstrate higher test scores compared to students who receive the traditional teaching method.”

Justification: Provide a logical explanation for the directional hypothesis based on the existing literature or theoretical framework . Discuss any previous findings, theories, or empirical evidence that support the predicted direction of the relationship or difference.

Objectives: Outline the specific objectives or aims of the study, which should align with the research question and hypothesis. These objectives help guide the research process and provide a clear focus for the study.

By including these elements in the introduction of a research study, the directional hypothesis is introduced effectively, providing a clear and justified prediction about the expected outcome of the research.

When formulating a directional hypothesis, researchers make a specific prediction about the expected relationship or difference between variables. They specify whether they expect an increase or decrease in the dependent variable, or whether one group will score higher or lower than another group

What is Directional Hypothesis?

With a correlational study, a directional hypothesis states that there is a positive (or negative) correlation between two variables. When a hypothesis states the direction of the results, it is referred to as a directional (one-tailed) hypothesis; this is because it states that the results go in one direction.

Definition:

A directional hypothesis is a one-tailed hypothesis that states the direction of the difference or relationship (e.g. boys are more helpful than girls).

Research Question: Does exercise have a positive impact on mood?

Directional Hypothesis: Engaging in regular exercise will result in an increase in positive mood compared to a sedentary lifestyle.

In this example, the directional hypothesis predicts that regular exercise will have a specific effect on mood, specifically leading to an increase in positive mood. The researcher expects that individuals who engage in regular exercise will experience improvements in their overall mood compared to individuals who lead a sedentary lifestyle.

It’s important to note that this is just one example, and directional hypotheses can be formulated in various research areas and contexts. The key is to make a specific prediction about the direction of the relationship or difference between variables based on prior knowledge or theoretical considerations.

Advantages of Directional Hypothesis

There are several advantages to using a directional hypothesis in research studies. Here are a few key benefits:

Specific Prediction:

A directional hypothesis allows researchers to make a specific prediction about the expected relationship or difference between variables. This provides a clear focus for the study and helps guide the research process. It also allows for more precise interpretation of the results.

Testable and Refutable:

Directional hypotheses can be tested and either supported or refuted by empirical evidence. Researchers can design their study and select appropriate statistical tests to specifically examine the predicted direction of the relationship or difference. This enhances the rigor and validity of the research.

Efficiency and Resource Allocation:

By making a specific prediction, researchers can allocate their resources more efficiently. They can focus on collecting data and conducting analyses that directly test the directional hypothesis, rather than exploring all possible directions or relationships. This can save time, effort, and resources.

Theory Development:

Directional hypotheses contribute to the development of theories and scientific knowledge. When a directional hypothesis is supported by empirical evidence, it provides support for existing theories or helps generate new theories. This advancement in knowledge can guide future research and understanding in the field.

Practical Applications:

Directional hypotheses can have practical implications and applications. If a hypothesis predicts a specific direction of change, such as the effectiveness of a treatment or intervention, it can inform decision-making and guide practical applications in fields such as medicine, psychology, or education.

Enhanced Communication:

Directional hypotheses facilitate clearer communication of research findings. When researchers have made specific predictions about the direction of the relationship or difference, they can effectively communicate their results to both academic and non-academic audiences. This promotes better understanding and application of the research outcomes.

It’s important to note that while directional hypotheses offer advantages, they also require stronger evidence to support them compared to non-directional hypotheses. Researchers should carefully consider the research context, existing literature, and theoretical considerations before formulating a directional hypothesis.

Disadvantages of Directional Hypothesis

While directional hypotheses have their advantages, there are also some potential disadvantages to consider:

Risk of Type I Error:

Directional hypotheses increase the risk of committing a Type I error , also known as a false positive. By focusing on a specific predicted direction, researchers may overlook the possibility of an opposite or null effect. If the actual relationship or difference does not align with the predicted direction, researchers may incorrectly conclude that there is no effect when, in fact, there may be.

Narrow Focus:

Directional hypotheses restrict the scope of investigation to a specific predicted direction. This narrow focus may overlook other potential relationships, nuances, or alternative explanations. Researchers may miss valuable insights or unexpected findings by excluding other possibilities from consideration.

Limited Generalizability:

Directional hypotheses may limit the generalizability of findings. If the study supports the predicted direction, the results may only apply to the specific context and conditions outlined in the hypothesis. Generalizing the findings to different populations, settings, or variables may require further research.

Biased Interpretation:

Directional hypotheses can introduce bias in the interpretation of results. Researchers may be inclined to selectively focus on evidence that supports the predicted direction while downplaying or ignoring contradictory evidence. This can hinder objectivity and lead to biased conclusions.

Increased Sample Size Requirements:

Directional hypotheses often require larger sample sizes compared to non-directional hypotheses. This is because statistical power needs to be sufficient to detect the predicted direction with a reasonable level of confidence. Larger samples can be more time-consuming and resource-intensive to obtain.

Reduced Flexibility:

Directional hypotheses limit flexibility in data analysis and statistical testing. Researchers may feel compelled to use specific statistical tests or analytical approaches that align with the predicted direction, potentially overlooking alternative methods that may be more appropriate or informative.

It’s important to weigh these disadvantages against the specific research context and objectives when deciding whether to use a directional hypothesis. In some cases, a non-directional hypothesis may be more suitable, allowing for a more exploratory and comprehensive investigation of the research question.

Non-Directional Hypothesis:

A non-directional hypothesis, also known as a two-tailed hypothesis, is a type of hypothesis that does not specify the direction of the relationship between variables or the difference between groups. Instead of predicting a specific direction, a non-directional hypothesis suggests that there will be a significant relationship or difference, without indicating whether it will be positive or negative, higher or lower, etc.

The introduction of a non-directional hypothesis in a research study provides an overview of the general prediction being made about the relationship between variables or the difference between groups, without specifying the direction. It sets the stage for the research question and outlines the expectation of a significant relationship or difference. The introduction typically includes the following elements:

Research Context:

Begin by introducing the general topic or research area that the study is focused on. Provide background information and highlight the significance of the research question.

Research Question:

Clearly state the specific research question that the study aims to answer. This question should be directly related to the variables being investigated.

Previous Research:

Summarize relevant literature or previous studies that have explored similar or related topics. This helps establish the existing knowledge base and provides a rationale for the hypothesis.

Hypothesis Statement:

Present the non-directional hypothesis clearly and concisely. State that there is an expected relationship or difference between variables or groups without specifying the direction. For example, if studying the relationship between socioeconomic status and academic achievement, a non-directional hypothesis could be, “There is a significant relationship between socioeconomic status and academic achievement.”

Justification:

Provide a logical explanation for the non-directional hypothesis based on the existing literature or theoretical framework. Discuss any previous findings, theories, or empirical evidence that support the notion of a relationship or difference between the variables or groups.

Objectives:

Outline the specific objectives or aims of the study, which should align with the research question and hypothesis. These objectives help guide the research process and provide a clear focus for the study.

By including these elements in the introduction of a research study, the non-directional hypothesis is introduced effectively, indicating the expectation of a significant relationship or difference without specifying the direction

What is Non-directional hypothesis?

In a non-directional hypothesis, researchers acknowledge that there may be an effect or relationship between variables but do not make a specific prediction about the direction of that effect. This allows for a more exploratory approach to data analysis and interpretation

If a hypothesis does not state a direction but simply says that one factor affects another, or that there is an association or correlation between two variables then it is called a non-directional (two-tailed) hypothesis.

Research Question: Is there a relationship between social media usage and self-esteem ?

Non-Directional Hypothesis: There is a significant relationship between social media usage and self-esteem.

In this example, the non-directional hypothesis suggests that there is a relationship between social media usage and self-esteem without specifying whether higher social media usage is associated with higher or lower self-esteem. The hypothesis acknowledges the possibility of an effect but does not make a specific prediction about the direction of that effect.

It’s important to note that this is just one example, and non-directional hypotheses can be formulated in various research areas and contexts. The key is to indicate the expectation of a significant relationship or difference without specifying the direction, allowing for a more exploratory approach to data analysis and interpretation.

Advantages of Non-directional hypothesis

Non-directional hypotheses, also known as two-tailed hypotheses, offer several advantages in research studies. Here are some of the key advantages:

Flexibility in Data Analysis:

Non-directional hypotheses allow for flexibility in data analysis. Researchers are not constrained by a specific predicted direction and can explore the relationship or difference in various ways. This flexibility enables a more comprehensive examination of the data, considering both positive and negative associations or differences.

Objective and Open-Minded Approach:

Non-directional hypotheses promote an objective and open-minded approach to research. Researchers do not have preconceived notions about the direction of the relationship or difference, which helps mitigate biases in data interpretation. They can objectively analyze the data without being influenced by their initial expectations.

Comprehensive Understanding:

By not specifying the direction, non-directional hypotheses facilitate a comprehensive understanding of the relationship or difference being investigated. Researchers can explore and consider all possible outcomes, leading to a more nuanced interpretation of the findings. This broader perspective can provide deeper insights into the research question.

Greater Sensitivity:

Non-directional hypotheses can be more sensitive to detecting unexpected or surprising relationships or differences. Researchers are not solely focused on confirming a specific predicted direction, but rather on uncovering any significant association or difference. This increased sensitivity allows for the identification of novel patterns and relationships that may have been overlooked with a directional hypothesis.

Replication and Generalizability:

Non-directional hypotheses support replication studies and enhance the generalizability of findings. By not restricting the investigation to a specific predicted direction, the results can be more applicable to different populations, contexts, or conditions. This broader applicability strengthens the validity and reliability of the research.

Hypothesis Generation:

Non-directional hypotheses can serve as a foundation for generating new hypotheses and research questions. Significant findings without a specific predicted direction can lead to further investigations and the formulation of more focused directional hypotheses in subsequent studies.

It’s important to consider the specific research context and objectives when deciding between a directional or non-directional hypothesis. Non-directional hypotheses are particularly useful when researchers are exploring new areas or when there is limited existing knowledge about the relationship or difference being studied.

Disadvantages of Non-directional hypothesis

Non-directional hypotheses have their advantages, there are also some potential disadvantages to consider:

Lack of Specificity: Non-directional hypotheses do not provide a specific prediction about the direction of the relationship or difference between variables. This lack of specificity may limit the interpretability and practical implications of the findings. Stakeholders may desire clear guidance on the expected direction of the effect.

Non-directional hypotheses often require larger sample sizes compared to directional hypotheses. This is because statistical power needs to be sufficient to detect any significant relationship or difference, regardless of the direction. Obtaining larger samples can be more time-consuming, resource-intensive, and costly.

Reduced Precision:

By not specifying the direction, non-directional hypotheses may result in less precise findings. Researchers may obtain statistically significant results indicating a relationship or difference, but the lack of direction may hinder their ability to understand the practical implications or mechanism behind the effect.

Potential for Post-hoc Interpretation:

Non-directional hypotheses can increase the risk of post-hoc interpretation of results. Researchers may be tempted to selectively interpret and highlight only the significant findings that support their preconceived notions or expectations, leading to biased interpretations.

Limited Theoretical Guidance:

Non-directional hypotheses may lack theoretical guidance in terms of understanding the underlying mechanisms or causal pathways. Without a specific predicted direction, it can be challenging to develop a comprehensive theoretical framework to explain the relationship or difference being studied.

Potential Missed Opportunities:

Non-directional hypotheses may limit the exploration of specific directions or subgroups within the data. By not focusing on a specific direction, researchers may miss important nuances or interactions that could contribute to a deeper understanding of the phenomenon under investigation.

It’s important to carefully consider the research question, available literature, and research objectives when deciding whether to use a non-directional hypothesis. Depending on the context and goals of the study, a non-directional hypothesis may be appropriate, but researchers should also be aware of the potential limitations and address them accordingly in their research design and interpretation of results.

Difference between directional and non-directional hypothesis

the main difference between a directional hypothesis and a non-directional hypothesis lies in the specificity of the prediction made about the relationship between variables or the difference between groups.

Directional Hypothesis:

A directional hypothesis, also known as a one-tailed hypothesis, makes a specific prediction about the direction of the relationship or difference. It states the expected outcome, whether it is a positive or negative relationship, a higher or lower value, an increase or decrease, etc. The directional hypothesis guides the research in a focused manner, specifying the direction to be tested.

Example: “Students who receive tutoring will demonstrate higher test scores compared to students who do not receive tutoring.”

A non-directional hypothesis, also known as a two-tailed hypothesis, does not specify the direction of the relationship or difference. It acknowledges the possibility of a relationship or difference between variables without predicting a specific direction. The non-directional hypothesis allows for exploration and analysis of both positive and negative associations or differences.

Example: “There is a significant relationship between sleep quality and academic performance.”

In summary, a directional hypothesis makes a specific prediction about the direction of the relationship or difference, while a non-directional hypothesis suggests a relationship or difference without specifying the direction. The choice between the two depends on the research question, existing literature, and the researcher’s objectives. Directional hypotheses provide a focused prediction, while non-directional hypotheses allow for more exploratory analysis .

When to use Directional Hypothesis?

A directional hypothesis is appropriate to use in specific situations where researchers have a clear theoretical or empirical basis for predicting the direction of the relationship or difference between variables. Here are some scenarios where a directional hypothesis is commonly employed:

Prior Research and Theoretical Framework: When previous studies, existing theories, or established empirical evidence strongly suggest a specific direction of the relationship or difference, a directional hypothesis can be formulated. Researchers can build upon the existing knowledge base and make a focused prediction based on this prior information.

Cause-and-Effect Relationships: In studies aiming to establish cause-and-effect relationships, directional hypotheses are often used. When there is a clear theoretical understanding of the causal relationship between variables, researchers can predict the expected direction of the effect based on the proposed mechanism.

Specific Research Objectives: If the research study has specific objectives that require a clear prediction about the direction, a directional hypothesis can be appropriate. For instance, if the aim is to test the effectiveness of a particular intervention or treatment, a directional hypothesis can guide the evaluation by predicting the expected positive or negative outcome.

Practical Applications: Directional hypotheses are useful when the research findings have direct practical implications. For example, in fields such as medicine, psychology, or education, researchers may formulate directional hypotheses to predict the effects of certain interventions or treatments on patient outcomes or educational achievement.

Hypothesis-Testing Approach: Researchers who adopt a hypothesis-testing approach, where they aim to confirm or disconfirm specific predictions, often use directional hypotheses. This approach involves formulating a specific hypothesis and conducting statistical tests to determine whether the data support or refute the predicted direction of the relationship or difference.

When to use non directional hypothesis?

A non-directional hypothesis, also known as a two-tailed hypothesis, is appropriate to use in several situations where researchers do not have a specific prediction about the direction of the relationship or difference between variables. Here are some scenarios where a non-directional hypothesis is commonly employed:

Exploratory Research:

When the research aims to explore a new area or investigate a relationship that has limited prior research or theoretical guidance, a non-directional hypothesis is often used. It allows researchers to gather initial data and insights without being constrained by a specific predicted direction.

Preliminary Studies:

Non-directional hypotheses are useful in preliminary or pilot studies that seek to gather preliminary evidence and generate hypotheses for further investigation. By using a non-directional hypothesis, researchers can gather initial data to inform the development of more specific hypotheses in subsequent studies.

Neutral Expectations:

If researchers have no theoretical or empirical basis to predict the direction of the relationship or difference, a non-directional hypothesis is appropriate. This may occur in situations where there is a lack of prior research, conflicting findings, or inconclusive evidence to support a specific direction.

Comparative Studies:

In studies where the objective is to compare two or more groups or conditions, a non-directional hypothesis is commonly used. The focus is on determining whether a significant difference exists, without making specific predictions about which group or condition will have higher or lower values.

Data-Driven Approach:

When researchers adopt a data-driven or exploratory approach to analysis, non-directional hypotheses are preferred. Instead of testing specific predictions, the aim is to explore the data, identify patterns, and generate hypotheses based on the observed relationships or differences.

Hypothesis-Generating Studies:

Non-directional hypotheses are often used in studies aimed at generating new hypotheses and research questions. By exploring associations or differences without specifying the direction, researchers can identify potential relationships or factors that can serve as a basis for future research.

Strategies to improve directional and non-directional hypothesis

To improve the quality of both directional and non-directional hypotheses, researchers can employ various strategies. Here are some strategies to enhance the formulation of hypotheses:

Strategies to Improve Directional Hypotheses:

Review existing literature:.

Conduct a thorough review of relevant literature to identify previous research findings, theories, and empirical evidence related to the variables of interest. This will help inform and support the formulation of a specific directional hypothesis based on existing knowledge.

Develop a Theoretical Framework:

Build a theoretical framework that outlines the expected causal relationship between variables. The theoretical framework should provide a clear rationale for predicting the direction of the relationship based on established theories or concepts.

Conduct Pilot Studies:

Conducting pilot studies or preliminary research can provide valuable insights and data to inform the formulation of a directional hypothesis. Initial findings can help researchers identify patterns or relationships that support a specific predicted direction.

Seek Expert Input:

Seek input from experts or colleagues in the field who have expertise in the area of study. Discuss the research question and hypothesis with them to obtain valuable insights, perspectives, and feedback that can help refine and improve the directional hypothesis.

Clearly Define Variables:

Clearly define and operationalize the variables in the hypothesis to ensure precision and clarity. This will help avoid ambiguity and ensure that the hypothesis is testable and measurable.

Strategies to Improve Non-Directional Hypotheses:

Preliminary exploration:.

Conduct initial exploratory research to gather preliminary data and insights on the relationship or difference between variables. This can provide a foundation for formulating a non-directional hypothesis based on observed patterns or trends.

Analyze Existing Data:

Analyze existing datasets to identify potential relationships or differences. Exploratory data analysis techniques such as data visualization, descriptive statistics, and correlation analysis can help uncover initial insights that can guide the formulation of a non-directional hypothesis.

Use Exploratory Research Designs:

Employ exploratory research designs such as qualitative studies, case studies, or grounded theory approaches. These designs allow researchers to gather rich data and explore relationships or differences without preconceived notions about the direction.

Consider Alternative Explanations:

When formulating a non-directional hypothesis, consider alternative explanations or potential factors that may influence the relationship or difference between variables. This can help ensure a comprehensive and nuanced understanding of the phenomenon under investigation.

Refine Based on Initial Findings:

Refine the non-directional hypothesis based on initial findings and observations from exploratory analyses. These findings can guide the formulation of more specific hypotheses in subsequent studies or inform the direction of further research.

In conclusion, both directional and non-directional hypotheses have their merits and are valuable in different research contexts.

 Here’s a summary of the key points regarding directional and non-directional hypotheses:

• A directional hypothesis makes a specific prediction about the direction of the relationship or difference between variables.
• It is appropriate when there is a clear theoretical or empirical basis for predicting the direction.
• Directional hypotheses provide a focused approach, guiding the research towards confirming or refuting a specific predicted direction.
• They are useful in studies where cause-and-effect relationships are being examined or when specific practical implications are desired.
• Directional hypotheses require careful consideration of prior research, theoretical frameworks, and available evidence.
• A non-directional hypothesis does not specify the direction of the relationship or difference between variables.
• It is employed when there is limited prior knowledge, conflicting findings, or a desire for exploratory analysis.
• Non-directional hypotheses allow for flexibility and open-mindedness in exploring the data, considering both positive and negative associations or differences.
• They are suitable for preliminary studies, exploratory research, or when the research question does not have a clear predicted direction.
• Non-directional hypotheses are beneficial for generating new hypotheses, replication studies, and enhancing generalizability.

In both cases, it is essential to ensure that hypotheses are clear, testable, and aligned with the research objectives. Researchers should also be open to revising and refining hypotheses based on the findings and feedback obtained during the research process. The choice between a directional and non-directional hypothesis depends on factors such as the research question, available literature, theoretical frameworks, and the specific objectives of the study. Researchers should carefully consider these factors to determine the most appropriate type of hypothesis to use in their research

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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