## psychologyrocks

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

writing-hypotheses-revision-sheet

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## Directional Hypothesis: Definition and 10 Examples

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.

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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]

- Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Social-Emotional Learning (Definition, Examples, Pros & Cons)
- Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ What is Educational Psychology?
- Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ What is IQ? (Intelligence Quotient)
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## 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.

On This Page:

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

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

## Some key points about hypotheses:

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

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

## Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

## Null Hypothesis

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

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

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

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

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

## Nondirectional Hypothesis

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

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

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

## Directional Hypothesis

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

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

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

## Falsifiability

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

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

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

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

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

## Can a Hypothesis be Proven?

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

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

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

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

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

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

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

## How to Write a Hypothesis

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

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

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

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

## More Examples

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

## Directional and non-directional hypothesis: A Comprehensive Guide

Karolina Konopka

Customer support manager

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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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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## 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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## Non Directional Hypothesis

In the realm of hypothesis formulation, non-directional hypotheses offer a distinct perspective. These hypotheses suggest a relationship between variables without specifying the nature or direction of that relationship. This guide delves into non-directional hypothesis examples across various fields, outlines a step-by-step approach to crafting them, and provides expert tips to ensure your non-directional hypotheses are robust and insightful. Explore the world of Thesis statement hypotheses that explore connections without predetermined expectations.

## What is the Non-Directional Hypothesis? – Definition

A non-directional hypothesis, also known as a two tailed hypothesis , is a type of hypothesis that predicts a relationship between variables without specifying the direction of that relationship. Unlike directional hypotheses that predict a specific outcome, non-directional hypotheses simply suggest that a relationship exists without indicating whether one variable will increase or decrease in response to changes in the other variable.

## What is an Example of a Non-Directional Hypothesis Statement?

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“An increase in exercise frequency is associated with changes in weight.”

In this non-directional hypothesis, the statement suggests that a relationship exists between exercise frequency and weight changes but doesn’t specify whether increased exercise will lead to weight loss or weight gain. It leaves the direction of the relationship open for empirical investigation and data analysis.

## 100 Non Directional Hypothesis Statement Examples

Non-directional hypotheses explore relationships between variables without predicting the specific outcome. These simple hypothesis offer flexibility, allowing researchers to uncover unforeseen connections. Discover a range of non-directional hypothesis examples that span disciplines, enabling empirical exploration and evidence-based conclusions.

- Impact of Stress on Sleep Quality : Stress levels are related to changes in sleep quality among college students.
- Relationship Between Social Media Use and Loneliness : Social media use is associated with variations in reported feelings of loneliness.
- Connection Between Parenting Styles and Adolescent Self-Esteem : Different parenting styles correlate with differences in adolescent self-esteem levels.
- Effects of Temperature on Productivity : Temperature variations affect productivity levels in office environments.
- Link Between Screen Time and Eye Strain : Screen time is related to variations in reported eye strain among digital device users.
- Influence of Study Techniques on Exam Performance : Study techniques correlate with differences in exam performance among students.
- Relationship Between Classroom Environment and Student Engagement : Classroom environment is associated with variations in student engagement levels.
- Impact of Music Tempo on Heart Rate : Music tempo relates to changes in heart rate during exercise.
- Connection Between Diet and Cholesterol Levels : Dietary choices are related to variations in cholesterol levels among adults.
- Effects of Outdoor Exposure on Mood : Outdoor exposure is associated with changes in reported mood among urban dwellers.
- Relationship Between Personality Traits and Leadership Styles : Personality traits are associated with differences in preferred leadership styles among professionals.
- Impact of Time Management Strategies on Academic Performance : Time management strategies correlate with variations in academic performance among college students.
- Connection Between Cultural Exposure and Empathy Levels : Cultural exposure relates to changes in reported empathy levels among individuals.
- Effects of Nutrition Education on Dietary Choices : Nutrition education is associated with variations in dietary choices among adolescents.
- Link Between Social Support and Stress Levels : Social support is related to differences in reported stress levels among working adults.
- Influence of Exercise Intensity on Mood : Exercise intensity correlates with variations in reported mood among fitness enthusiasts.
- Relationship Between Parental Involvement and Academic Achievement : Parental involvement is associated with differences in academic achievement among schoolchildren.
- Impact of Sleep Duration on Cognitive Function : Sleep duration is related to changes in cognitive function among older adults.
- Connection Between Environmental Factors and Creativity : Environmental factors correlate with variations in reported creative thinking abilities among artists.
- Effects of Communication Styles on Conflict Resolution : Communication styles are associated with differences in conflict resolution outcomes among couples.
- Relationship Between Social Interaction and Life Satisfaction : Social interaction is related to variations in reported life satisfaction among elderly individuals.
- Impact of Classroom Seating Arrangements on Participation : Classroom seating arrangements correlate with differences in student participation levels.
- Connection Between Smartphone Use and Sleep Quality : Smartphone use is associated with changes in reported sleep quality among young adults.
- Effects of Mindfulness Practices on Stress Reduction : Mindfulness practices relate to variations in reported stress levels among participants.
- Link Between Gender and Communication Styles : Gender is related to differences in communication styles among individuals in group discussions.
- Influence of Advertising Exposure on Purchase Decisions : Advertising exposure correlates with variations in reported purchase decisions among consumers.
- Relationship Between Job Satisfaction and Employee Productivity : Job satisfaction is associated with differences in employee productivity levels.
- Impact of Social Support on Coping Mechanisms : Social support relates to variations in reported coping mechanisms among individuals facing challenges.
- Connection Between Classroom Environment and Student Creativity : Classroom environment is related to changes in student creativity levels.
- Effects of Exercise on Mood : Exercise is associated with variations in reported mood levels among participants.
- Relationship Between Music Preferences and Stress Levels : Music preferences are related to variations in reported stress levels among individuals.
- Impact of Nutrition Education on Food Choices : Nutrition education correlates with differences in dietary food choices among adolescents.
- Connection Between Physical Activity and Cognitive Function : Physical activity is associated with changes in cognitive function among older adults.
- Effects of Color Exposure on Mood : Color exposure relates to variations in reported mood levels among participants.
- Link Between Personality Traits and Career Choice : Personality traits are related to differences in career choices among individuals.
- Influence of Outdoor Recreation on Mental Well-being : Outdoor recreation is associated with variations in reported mental well-being among participants.
- Relationship Between Social Media Use and Self-Esteem : Social media use correlates with changes in reported self-esteem levels among young adults.
- Impact of Parenting Styles on Adolescent Risk Behavior : Parenting styles are related to variations in reported risk behaviors among adolescents.
- Connection Between Sleep Quality and Cognitive Performance : Sleep quality relates to changes in cognitive performance among students.
- Effects of Art Exposure on Creativity : Art exposure is associated with differences in reported creative thinking abilities among participants.
- Relationship Between Social Support and Mental Health : Social support is related to variations in reported mental health outcomes among individuals.
- Impact of Technology Use on Interpersonal Communication : Technology use correlates with differences in reported interpersonal communication skills among individuals.
- Connection Between Parental Attachment and Romantic Relationships : Parental attachment is associated with variations in the quality of romantic relationships among adults.
- Effects of Environmental Noise on Concentration : Environmental noise relates to changes in reported concentration levels among students.
- Link Between Music Exposure and Memory Performance : Music exposure is related to differences in memory performance among participants.
- Influence of Nutrition on Physical Fitness : Nutrition choices correlate with variations in reported physical fitness levels among athletes.
- Relationship Between Stress and Health Outcomes : Stress levels are associated with changes in reported health outcomes among individuals.
- Impact of Workplace Environment on Job Satisfaction : Workplace environment relates to differences in reported job satisfaction among employees.
- Connection Between Humor and Stress Reduction : Humor is related to variations in reported stress reduction among participants.
- Effects of Social Interaction on Emotional Well-being : Social interaction correlates with changes in reported emotional well-being among participants.
- Relationship Between Cultural Exposure and Cognitive Flexibility : Cultural exposure is related to variations in reported cognitive flexibility among individuals.
- Impact of Parent-Child Communication on Academic Achievement : Parent-child communication correlates with differences in academic achievement levels among students.
- Connection Between Personality Traits and Prosocial Behavior : Personality traits are associated with variations in reported prosocial behaviors among individuals.
- Effects of Nature Exposure on Stress Reduction : Nature exposure relates to changes in reported stress reduction among participants.
- Link Between Sleep Duration and Cognitive Performance : Sleep duration is related to differences in cognitive performance among participants.
- Influence of Social Media Use on Body Image : Social media use correlates with variations in reported body image satisfaction among young adults.
- Relationship Between Exercise and Mental Well-being : Exercise levels are associated with changes in reported mental well-being among participants.
- Impact of Cultural Competency Training on Patient Care : Cultural competency training relates to differences in patient care outcomes among healthcare professionals.
- Connection Between Perceived Social Support and Resilience : Perceived social support is related to variations in reported resilience levels among individuals.
- Effects of Environmental Factors on Mood : Environmental factors correlate with changes in reported mood levels among participants.
- Relationship Between Cultural Diversity and Team Performance : Cultural diversity is related to variations in reported team performance outcomes among professionals.
- Impact of Parental Involvement on Academic Motivation : Parental involvement correlates with differences in academic motivation levels among schoolchildren.
- Connection Between Mindfulness Practices and Anxiety Reduction : Mindfulness practices are associated with changes in reported anxiety levels among participants.
- Effects of Nutrition Education on Eating Habits : Nutrition education relates to variations in dietary eating habits among adolescents.
- Link Between Personality Traits and Learning Styles : Personality traits are related to differences in preferred learning styles among students.
- Influence of Nature Exposure on Creativity : Nature exposure correlates with variations in reported creative thinking abilities among individuals.
- Relationship Between Extracurricular Activities and Social Skills : Extracurricular activities are associated with changes in reported social skills among adolescents.
- Impact of Cultural Awareness Training on Stereotypes : Cultural awareness training relates to differences in perceived stereotypes among participants.
- Connection Between Sleep Quality and Emotional Regulation : Sleep quality is related to variations in reported emotional regulation skills among individuals.
- Effects of Music Exposure on Mood : Music exposure correlates with changes in reported mood levels among participants.
- Relationship Between Cultural Sensitivity and Cross-Cultural Communication : Cultural sensitivity is related to variations in reported cross-cultural communication skills among professionals.
- Impact of Parent-Child Bonding on Emotional Well-being : Parent-child bonding correlates with differences in reported emotional well-being levels among individuals.
- Connection Between Personality Traits and Conflict Resolution Styles : Personality traits are associated with variations in preferred conflict resolution styles among individuals.
- Effects of Mindfulness Practices on Focus and Concentration : Mindfulness practices relate to changes in reported focus and concentration levels among participants.
- Link Between Gender Identity and Career Aspirations : Gender identity is related to differences in reported career aspirations among individuals.
- Influence of Art Exposure on Emotional Expression : Art exposure correlates with variations in reported emotional expression abilities among participants.
- Relationship Between Peer Influence and Risky Behavior : Peer influence is associated with changes in reported engagement in risky behaviors among adolescents.
- Impact of Diversity Training on Workplace Harmony : Diversity training relates to differences in perceived workplace harmony among employees.
- Connection Between Sleep Patterns and Cognitive Performance : Sleep patterns are related to variations in cognitive performance among students.
- Effects of Exercise on Self-Esteem : Exercise correlates with changes in reported self-esteem levels among participants.
- Relationship Between Social Interaction and Well-being : Social interaction is related to variations in reported well-being levels among individuals.
- Impact of Parenting Styles on Adolescent Peer Relationships : Parenting styles correlate with differences in peer relationship quality among adolescents.
- Connection Between Personality Traits and Communication Effectiveness : Personality traits are associated with variations in communication effectiveness among professionals.
- Effects of Outdoor Activities on Stress Reduction : Outdoor activities relate to changes in reported stress reduction among participants.
- Link Between Music Exposure and Emotional Regulation : Music exposure is related to differences in reported emotional regulation skills among individuals.
- Influence of Family Dynamics on Academic Achievement : Family dynamics correlate with variations in academic achievement levels among students.
- Relationship Between Cultural Engagement and Empathy : Cultural engagement is associated with changes in reported empathy levels among individuals.
- Impact of Conflict Resolution Strategies on Relationship Satisfaction : Conflict resolution strategies relate to differences in reported relationship satisfaction levels among couples.
- Connection Between Sleep Quality and Physical Health : Sleep quality is related to variations in reported physical health outcomes among individuals.
- Effects of Social Support on Coping with Stress : Social support correlates with changes in reported coping strategies for stress among participants.
- Relationship Between Cultural Sensitivity and Patient Care : Cultural sensitivity is related to variations in reported patient care outcomes among healthcare professionals.
- Impact of Family Communication on Adolescent Well-being : Family communication correlates with differences in reported well-being levels among adolescents.
- Connection Between Personality Traits and Leadership Styles : Personality traits are associated with variations in preferred leadership styles among professionals.
- Effects of Nature Exposure on Attention Span : Nature exposure relates to changes in reported attention span among participants.
- Link Between Music Preference and Emotional Expression : Music preference is related to differences in reported emotional expression abilities among individuals.
- Influence of Peer Support on Academic Success : Peer support correlates with variations in reported academic success levels among students.
- Relationship Between Cultural Engagement and Creativity : Cultural engagement is associated with changes in reported creative thinking abilities among individuals.
- Impact of Conflict Resolution Skills on Relationship Satisfaction : Conflict resolution skills relate to differences in reported relationship satisfaction levels among couples.
- Connection Between Sleep Patterns and Stress Levels : Sleep patterns are related to variations in reported stress levels among individuals.
- Effects of Social Interaction on Happiness : Social interaction correlates with changes in reported happiness levels among participants.

## Non-Directional Hypothesis Statement Examples for Psychology

These examples pertain to psychological studies and cover various relationships between psychological hypothesis concepts. For instance, the first example suggests that attachment styles might be related to romantic satisfaction, but it doesn’t specify whether attachment styles would increase or decrease satisfaction.

- Relationship Between Attachment Styles and Romantic Satisfaction : Attachment styles are related to variations in reported romantic satisfaction levels among individuals in psychology studies.
- Impact of Personality Traits on Career Success : Personality traits correlate with differences in reported career success outcomes among psychology study participants.
- Connection Between Parenting Styles and Adolescent Self-Esteem : Parenting styles are associated with variations in reported self-esteem levels among adolescents in psychological research.
- Effects of Social Media Use on Body Image : Social media use relates to changes in reported body image satisfaction among young adults in psychology experiments.
- Link Between Sleep Patterns and Emotional Well-being : Sleep patterns are related to differences in reported emotional well-being levels among psychology research participants.
- Influence of Mindfulness Practices on Stress Reduction : Mindfulness practices correlate with variations in reported stress reduction among psychology study participants.
- Relationship Between Social Interaction and Mental Health : Social interaction is associated with changes in reported mental health outcomes among individuals in psychology studies.
- Impact of Parent-Child Bonding on Emotional Resilience : Parent-child bonding relates to differences in reported emotional resilience levels among psychology research participants.
- Connection Between Cultural Sensitivity and Empathy : Cultural sensitivity is related to variations in reported empathy levels among individuals in psychology experiments.
- Effects of Exercise on Mood : Exercise correlates with changes in reported mood levels among psychology study participants.

## Non-Directional Hypothesis Statement Examples in Research

These research hypothesis examples focus on research studies in general, covering a wide range of topics and relationships. For instance, the second example suggests that employee training might be related to workplace productivity, without indicating whether the training would lead to higher or lower productivity.

- Relationship Between Time Management and Academic Performance : Time management is related to variations in academic performance levels among research participants.
- Impact of Employee Training on Workplace Productivity : Employee training correlates with differences in reported workplace productivity outcomes among research subjects.
- Connection Between Media Exposure and Political Knowledge : Media exposure is associated with variations in reported political knowledge levels among research participants.
- Effects of Environmental Factors on Children’s Cognitive Development : Environmental factors relate to changes in reported cognitive development among research subjects.
- Link Between Parental Involvement and Student Motivation : Parental involvement is related to differences in reported student motivation levels among research participants.
- Influence of Cultural Immersion on Language Proficiency : Cultural immersion correlates with variations in reported language proficiency levels among research subjects.
- Relationship Between Leadership Styles and Team Performance : Leadership styles are associated with changes in reported team performance outcomes among research participants.
- Impact of Financial Literacy Education on Savings Habits : Financial literacy education relates to differences in reported savings habits among research subjects.
- Connection Between Stress Levels and Physical Health : Stress levels are related to variations in reported physical health outcomes among research participants.
- Effects of Music Exposure on Concentration : Music exposure correlates with changes in reported concentration levels among research subjects.

## Non-Directional Hypothesis Statement Examples for Research Methodology

These examples are specific to the methods used in conducting research. The eighth example states that randomization might relate to group equivalence, but it doesn’t specify whether randomization would lead to more equivalent or less equivalent groups.

- Relationship Between Sampling Techniques and Research Validity : Sampling techniques are related to variations in research validity outcomes in studies of research methodology.
- Impact of Data Collection Methods on Data Accuracy : Data collection methods correlate with differences in reported data accuracy in research methodology experiments.
- Connection Between Research Design and Study Reproducibility : Research design is associated with variations in reported study reproducibility in research methodology studies.
- Effects of Questionnaire Format on Response Consistency : Questionnaire format relates to changes in reported response consistency in research methodology research.
- Link Between Ethical Considerations and Research Credibility : Ethical considerations are related to differences in reported research credibility in studies of research methodology.
- Influence of Measurement Scales on Data Precision : Measurement scales correlate with variations in reported data precision in research methodology experiments.
- Relationship Between Experimental Controls and Internal Validity : Experimental controls are associated with changes in internal validity outcomes in research methodology studies.
- Impact of Randomization on Group Equivalence : Randomization relates to differences in reported group equivalence in research methodology research.
- Connection Between Qualitative Data Analysis Methods and Data Richness : Qualitative data analysis methods are related to variations in reported data richness in studies of research methodology.
- Effects of Hypothesis Formulation on Research Focus : Hypothesis formulation correlates with changes in reported research focus in research methodology experiments.

These non-directional hypothesis statement examples offer insights into the diverse array of relationships explored in psychology, research, and research methodology studies, fostering empirical discovery and contributing to the advancement of knowledge across various fields.

## Difference between Directional & Non-Directional Hypothesis

Directional and non-directional hypotheses are distinct approaches used in formulating hypotheses for research studies. Understanding the differences between them is essential for researchers to choose the appropriate type of causal hypothesis based on their study’s goals and prior knowledge.

- Direction: Directional hypotheses predict a specific relationship direction, while non-directional hypotheses do not specify a direction.
- Specificity: Directional hypotheses are more specific, while non-directional hypotheses are more general.
- Flexibility: Non-directional hypotheses allow for open-ended exploration, while directional hypotheses focus on confirming or refuting specific expectations.

## How to Write a Non-Directional Hypothesis Statement – Step by Step Guide

- Identify Variables: Clearly define the variables you’re investigating—usually, an independent variable (the one manipulated) and a dependent variable (the one measured).
- Indicate Relationship: State that a relationship exists between the variables without predicting a specific direction.
- Use General Language: Craft the statement in a way that encompasses various possible outcomes.
- Avoid Biased Language: Do not include words that suggest a stronger effect or specific outcome for either variable.
- Connect to Research: If applicable, link the hypothesis to existing research or theories that justify exploring the relationship.

## Tips for Writing a Non-Directional Hypothesis

- Start with Inquiry: Frame your hypothesis as an answer to a research question.
- Embrace Openness: Non-directional hypotheses are ideal when no strong expectation exists.
- Be Succinct: Keep the hypothesis statement concise and clear.
- Stay Neutral: Avoid implying that one variable will have a stronger impact.
- Allow Exploration: Leave room for various potential outcomes without preconceived notions.
- Tailor to Context: Ensure the hypothesis aligns with your research context and goals.

Non-directional hypotheses are particularly useful in exploratory research, where researchers aim to discover relationships without imposing specific expectations. They allow for unbiased investigation and the potential to uncover unexpected patterns or connections.

Remember that whether you choose a directional or non-directional hypothesis, both play critical roles in shaping the research process, guiding study design, data collection, and analysis. The choice depends on the research’s nature, goals, and existing knowledge in the field. You may also be interested in our science hypothesis .

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## 16.3: The Process of Null Hypothesis Testing

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- Russell A. Poldrack
- Stanford University

We can break the process of null hypothesis testing down into a number of steps:

- Formulate a hypothesis that embodies our prediction ( before seeing the data )
- Collect some data relevant to the hypothesis
- Specify null and alternative hypotheses
- Fit a model to the data that represents the alternative hypothesis and compute a test statistic
- Compute the probability of the observed value of that statistic assuming that the null hypothesis is true
- Assess the “statistical significance” of the result

For a hands-on example, let’s use the NHANES data to ask the following question: Is physical activity related to body mass index? In the NHANES dataset, participants were asked whether they engage regularly in moderate or vigorous-intensity sports, fitness or recreational activities (stored in the variable P h y s A c t i v e PhysActive ). The researchers also measured height and weight and used them to compute the Body Mass Index (BMI):

B M I = w e i g h t ( k g ) h e i g h t ( m ) 2 BMI = \frac{weight(kg)}{height(m)^2}

## 16.3.1 Step 1: Formulate a hypothesis of interest

For step 1, we hypothesize that BMI is greater for people who do not engage in physical activity, compared to those who do.

## 16.3.2 Step 2: Collect some data

For step 2, we collect some data. In this case, we will sample 250 individuals from the NHANES dataset. Figure 16.1 shows an example of such a sample, with BMI shown separately for active and inactive individuals.

## 16.3.3 Step 3: Specify the null and alternative hypotheses

For step 3, we need to specify our null hypothesis (which we call H 0 H _0 ) and our alternative hypothesis (which we call H A H_A ). H 0 H _0 is the baseline against which we test our hypothesis of interest: that is, what would we expect the data to look like if there was no effect? The null hypothesis always involves some kind of equality (=, ≤ \le , or ≥ \ge ). H A H_A describes what we expect if there actually is an effect. The alternative hypothesis always involves some kind of inequality ( ≠ \ne , >, or <). Importantly, null hypothesis testing operates under the assumption that the null hypothesis is true unless the evidence shows otherwise.

We also have to decide whether to use directional or non-directional hypotheses. A non-directional hypothesis simply predicts that there will be a difference, without predicting which direction it will go. For the BMI/activity example, a non-directional null hypothesis would be:

H 0 : B M I a c t i v e = B M I i n a c t i v e H0 : BMI_{active} = BMI_{inactive}

and the corresponding non-directional alternative hypothesis would be:

H A : B M I a c t i v e ≠ B M I i n a c t i v e HA: BMI_{active} \neq BMI_{inactive}

A directional hypothesis, on the other hand, predicts which direction the difference would go. For example, we have strong prior knowledge to predict that people who engage in physical activity should weigh less than those who do not, so we would propose the following directional null hypothesis:

H 0 : B M I a c t i v e ≥ B M I i n a c t i v e H0: BMI_{active} \ge BMI_{inactive}

and directional alternative:

H A : B M I a c t i v e < B M I i n a c t i v e HA : BMI_{active} < BMI_{inactive}

As we will see later, testing a non-directional hypothesis is more conservative, so this is generally to be preferred unless there is a strong a priori reason to hypothesize an effect in a particular direction. Any direction hypotheses should be specified prior to looking at the data!

## 16.3.4 Step 4: Fit a model to the data and compute a test statistic

For step 4, we want to use the data to compute a statistic that will ultimately let us decide whether the null hypothesis is rejected or not. To do this, the model needs to quantify the amount of evidence in favor of the alternative hypothesis, relative to the variability in the data. Thus we can think of the test statistic as providing a measure of the size of the effect compared to the variability in the data. In general, this test statistic will have a probability distribution associated with it, because that allows us to determine how likely our observed value of the statistic is under the null hypothesis.

For the BMI example, we need a test statistic that allows us to test for a difference between two means, since the hypotheses are stated in terms of mean BMI for each group. One statistic that is often used to compare two means is the t-statistic , first developed by the statistician William Sealy Gossett, who worked for the Guiness Brewery in Dublin and wrote under the pen name “Student” - hence, it is often called “Student’s t-statistic”. The t-statistic is appropriate for comparing the means of two groups when the sample sizes are relatively small and the population standard deviation is unknown. The t-statistic for comparison of two independent groups is computed as:

t = X 1 ‾ − X 2 ‾ S 1 2 n 1 + S 2 2 n 2 t = \frac{\bar{X_1} - \bar{X_2}}{\sqrt{\frac{S_1^2}{n_1} + \frac{S_2^2}{n_2}}}

where X ‾ 1 \bar{X}_1 and X ‾ 2 \bar{X}_2 are the means of the two groups, S 1 2 S ^2_1 and S 2 2 S ^2_2 are the estimated variances of the groups, and n 1 n _1 and n 2 n _2 are the sizes of the two groups. Note that the denominator is basically an average of the standard error of the mean for the two samples. Thus, one can view the the t-statistic as a way of quantifying how large the difference between groups is in relation to the sampling variability of the means that are being compared.

The t-statistic is distributed according to a probability distribution known as a t distribution. The t distribution looks quite similar to a normal distribution, but it differs depending on the number of degrees of freedom, which for this example is the number of observations minus 2, since we have computed two means and thus given up two degrees of freedom. When the degrees of freedom are large (say 1000), then the t distribution looks essentialy like the normal distribution, but when they are small then the t distribution has longer tails than the normal (see Figure 16.2).

## 16.3.5 Step 5: Determine the probability of the data under the null hypothesis

This is the step where NHST starts to violate our intuition – rather than determining the likelihood that the null hypothesis is true given the data, we instead determine the likelihood of the data under the null hypothesis - because we started out by assuming that the null hypothesis is true! To do this, we need to know the probability distribution for the statistic under the null hypothesis, so that we can ask how likely the data are under that distribution. Before we move to our BMI data, let’s start with some simpler examples.

## 16.3.5.1 Randomization: A very simple example

Let’s say that we wish to determine whether a coin is fair. To collect data, we flip the coin 100 times, and we count 70 heads. In this example, H 0 : P ( h e a d s ) = 0.5 H_0: P(heads)=0.5 and H A : P ( h e a d s ) ≠ 0.5 H_A: P(heads) \neq 0.5 , and our test statistic is simply the number of heads that we counted. The question that we then want to ask is: How likely is it that we would observe 70 heads if the true probability of heads is 0.5. We can imagine that this might happen very occasionally just by chance, but doesn’t seem very likely. To quantify this probability, we can use the binomial distribution :

P ( X < k ) = ∑ i = 0 k ( N k ) p i ( 1 − p ) ( n − i ) P(X < k) = \sum_{i=0}^k \binom{N}{k} p^i (1-p)^{(n-i)} This equation will tell us the likelihood of a certain number of heads or fewer, given a particular probability of heads. However, what we really want to know is the probability of a certain number or more, which we can obtain by subtracting from one, based on the rules of probability:

P ( X ≥ k ) = 1 − P ( X < k ) P(X \ge k) = 1 - P(X < k)

We can compute the probability for our example using the pbinom() function. The probability of 69 or fewer heads given P(heads)=0.5 is 0.999961, so the probability of 70 or more heads is simply one minus that value (0.000039) This computation shows us that the likelihood of getting 70 heads if the coin is indeed fair is very small.

Now, what if we didn’t have the pbinom() function to tell us the probability of that number of heads? We could instead determine it by simulation – we repeatedly flip a coin 100 times using a true probability of 0.5, and then compute the distribution of the number of heads across those simulation runs. Figure 16.3 shows the result from this simulation. Here we can see that the probability computed via simulation (0.000030) is very close to the theoretical probability (.00004).

Let’s do the analogous computation for our BMI example. First we compute the t statistic using the values from our sample that we calculated above, where we find that (t = 3.86). The question that we then want to ask is: What is the likelihood that we would find a t statistic of this size, if the true difference between groups is zero or less (i.e. the directional null hypothesis)?

We can use the t distribution to determine this probability. Our sample size is 250, so the appropriate t distribution has 248 degrees of freedom because lose one for each of the two means that we computed. We can use the pt() function in R to determine the probability of finding a value of the t-statistic greater than or equal to our observed value. Note that we want to know the probability of a value greater than our observed value, but by default pt() gives us the probability of a value less than the one that we provide it, so we have to tell it explicitly to provide us with the “upper tail” probability (by setting lower.tail = FALSE ). We find that (p(t > 3.86, df = 248) = 0.000), which tells us that our observed t-statistic value of 3.86 is relatively unlikely if the null hypothesis really is true.

In this case, we used a directional hypothesis, so we only had to look at one end of the null distribution. If we wanted to test a non-directional hypothesis, then we would need to be able to identify how unexpected the size of the effect is, regardless of its direction. In the context of the t-test, this means that we need to know how likely it is that the statistic would be as extreme in either the positive or negative direction. To do this, we multiply the observed t value by -1, since the t distribution is centered around zero, and then add together the two tail probabilities to get a two-tailed p-value: (p(t > 3.86 or t< -3.86, df = 248) = 0.000). Here we see that the p value for the two-tailed test is twice as large as that for the one-tailed test, which reflects the fact that an extreme value is less surprising since it could have occurred in either direction.

How do you choose whether to use a one-tailed versus a two-tailed test? The two-tailed test is always going to be more conservative, so it’s always a good bet to use that one, unless you had a very strong prior reason for using a one-tailed test. In that case, you should have written down the hypothesis before you ever looked at the data. In Chapter 32 we will discuss the idea of pre-registration of hypotheses, which formalizes the idea of writing down your hypotheses before you ever see the actual data. You should never make a decision about how to perform a hypothesis test once you have looked at the data, as this can introduce serious bias into the results.

## 16.3.5.2 Computing p-values using randomization

So far we have seen how we can use the t-distribution to compute the probability of the data under the null hypothesis, but we can also do this using simulation. The basic idea is that we generate simulated data like those that we would expect under the null hypothesis, and then ask how extreme the observed data are in comparison to those simulated data. The key question is: How can we generate data for which the null hypothesis is true? The general answer is that we can randomly rearrange the data in a particular way that makes the data look like they would if the null was really true. This is similar to the idea of bootstrapping, in the sense that it uses our own data to come up with an answer, but it does it in a different way.

## 16.3.5.3 Randomization: a simple example

Let’s start with a simple example. Let’s say that we want to compare the mean squatting ability of football players with cross-country runners, with H 0 : μ F B ≤ μ X C H_0: \mu_{FB} \le \mu_{XC} and H A : μ F B > μ X C H _A: \mu_{FB} > \mu_{XC} . We measure the maximum squatting ability of 5 football players and 5 cross-country runners (which we will generate randomly, assuming that μ F B = 300 \mu_{FB} = 300 , μ X C = 140 \mu_{XC} = 140 , and σ = 30 \sigma = 30 ).

From the plot in Figure 16.4 it’s clear that there is a large difference between the two groups. We can do a standard t-test to test our hypothesis, using the t.test() command in R, which gives the following result:

If we look at the p-value reported here, we see that the likelihood of such a difference under the null hypothesis is very small, using the t distribution to define the null.

Now let’s see how we could answer the same question using randomization. The basic idea is that if the null hypothesis of no difference between groups is true, then it shouldn’t matter which group one comes from (football players versus cross-country runners) – thus, to create data that are like our actual data but also conform to the null hypothesis, we can randomly reorder the group labels for the individuals in the dataset, and then recompute the difference between the groups. The results of such a shuffle are shown in Figure ?? .

After scrambling the labels, we see that the two groups are now much more similar, and in fact the cross-country group now has a slightly higher mean. Now let’s do that 10000 times and store the t statistic for each iteration; this may take a moment to complete. Figure 16.5 shows the histogram of the t-values across all of the random shuffles. As expected under the null hypothesis, this distribution is centered at zero (the mean of the distribution is -0.016. From the figure we can also see that the distribution of t values after shuffling roughly follows the theoretical t distribution under the null hypothesis (with mean=0), showing that randomization worked to generate null data. We can compute the p-value from the randomized data by measuring how many of the shuffled values are at least as extreme as the observed value: p(t > 5.14, df = 8) using randomization = 0.00380. This p-value is very similar to the p-value that we obtained using the t distribution, and both are quite extreme, suggesting that the observed data are very unlikely to have arisen if the null hypothesis is true - and in this case we know that it’s not true, because we generated the data.

## 16.3.5.3.1 Randomization: BMI/activity example

Now let’s use randomization to compute the p-value for the BMI/activity example. In this case, we will randomly shuffle the PhysActive variable and compute the difference between groups after each shuffle, and then compare our observed t statistic to the distribution of t statistics from the shuffled datasets. Figure 16.6 shows the distribution of t values from the shuffled samples, and we can also compute the probability of finding a value as large or larger than the observed value. The p-value obtained from randomization (0.0000) is very similar to the one obtained using the t distribution (0.0001). The advantage of the randomization test is that it doesn’t require that we assume that the data from each of the groups are normally distributed, though the t-test is generally quite robust to violations of that assumption. In addition, the randomization test can allow us to compute p-values for statistics when we don’t have a theoretical distribution like we do for the t-test.

We do have to make one main assumption when we use the randomization test, which we refer to as exchangeability . This means that all of the observations are distributed in the same way, such that we can interchange them without changing the overall distribution. The main place where this can break down is when there are related observations in the data; for example, if we had data from individuals in 4 different families, then we couldn’t assume that individuals were exchangeable, because siblings would be closer to each other than they are to individuals from other families. In general, if the data were obtained by random sampling, then the assumption of exchangeability should hold.

## 16.3.6 Step 6: Assess the “statistical significance” of the result

The next step is to determine whether the p-value that results from the previous step is small enough that we are willing to reject the null hypothesis and conclude instead that the alternative is true. How much evidence do we require? This is one of the most controversial questions in statistics, in part because it requires a subjective judgment – there is no “correct” answer.

Historically, the most common answer to this question has been that we should reject the null hypothesis if the p-value is less than 0.05. This comes from the writings of Ronald Fisher, who has been referred to as “the single most important figure in 20th century statistics” (Efron 1998) :

“If P is between .1 and .9 there is certainly no reason to suspect the hypothesis tested. If it is below .02 it is strongly indicated that the hypothesis fails to account for the whole of the facts. We shall not often be astray if we draw a conventional line at .05 … it is convenient to draw the line at about the level at which we can say: Either there is something in the treatment, or a coincidence has occurred such as does not occur more than once in twenty trials” (Fisher 1925)

However, Fisher never intended p < 0.05 p < 0.05 to be a fixed rule:

“no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas” [fish:1956]

Instead, it is likely that it became a ritual due to the reliance upon tables of p-values that were used before computing made it easy to compute p values for arbitrary values of a statistic. All of the tables had an entry for 0.05, making it easy to determine whether one’s statistic exceeded the value needed to reach that level of significance.

The choice of statistical thresholds remains deeply controversial, and recently (Benjamin et al., 2018) it has been proposed that the standard threshold be changed from .05 to .005, making it substantially more stringent and thus more difficult to reject the null hypothesis. In large part this move is due to growing concerns that the evidence obtained from a significant result at p < . 05 32.

## 16.3.6.1 Hypothesis testing as decision-making: The Neyman-Pearson approach

Whereas Fisher thought that the p-value could provide evidence regarding a specific hypothesis, the statisticians Jerzy Neyman and Egon Pearson disagreed vehemently. Instead, they proposed that we think of hypothesis testing in terms of its error rate in the long run:

“no test based upon a theory of probability can by itself provide any valuable evidence of the truth or falsehood of a hypothesis. But we may look at the purpose of tests from another viewpoint. Without hoping to know whether each separate hypothesis is true or false, we may search for rules to govern our behaviour with regard to them, in following which we insure that, in the long run of experience, we shall not often be wrong” (Neyman and Pearson 1933)

That is: We can’t know which specific decisions are right or wrong, but if we follow the rules, we can at least know how often our decisions will be wrong on average.

To understand the decision making framework that Neyman and Pearson developed, we first need to discuss statistical decision making in terms of the kinds of outcomes that can occur. There are two possible states of reality ( H 0 H _0 is true, or H 0 H _0 is false), and two possible decisions (reject H 0 H _0 , or fail to reject H 0 H _0 ). There are two ways in which we can make a correct decision:

- We can decide to reject H 0 H _0 when it is false (in the language of decision theory, we call this a hit )
- We can fail to reject H 0 H _0 when it is true (we call this a correct rejection )

There are also two kinds of errors we can make:

- We can decide to reject H 0 H _0 when it is actually true (we call this a false alarm , or Type I error )
- We can fail to reject H 0 H _0 when it is actually false (we call this a miss , or Type II error )

Neyman and Pearson coined two terms to describe the probability of these two types of errors in the long run:

- P(Type I error) = α \alpha
- P(Type II error) = β \beta

That is, if we set α 18.3, which is the complement of Type II error.

## 16.3.7 What does a significant result mean?

There is a great deal of confusion about what p-values actually mean (Gigerenzer, 2004). Let’s say that we do an experiment comparing the means between conditions, and we find a difference with a p-value of .01. There are a number of possible interpretations.

## 16.3.7.1 Does it mean that the probability of the null hypothesis being true is .01?

No. Remember that in null hypothesis testing, the p-value is the probability of the data given the null hypothesis ( P ( d a t a | H 0 ) P(data|H_0) ). It does not warrant conclusions about the probability of the null hypothesis given the data ( P ( H 0 | d a t a ) P(H_0|data) ). We will return to this question when we discuss Bayesian inference in a later chapter, as Bayes theorem lets us invert the conditional probability in a way that allows us to determine the latter probability.

## 16.3.7.2 Does it mean that the probability that you are making the wrong decision is .01?

No. This would be P ( H 0 | d a t a ) P(H_0|data) , but remember as above that p-values are probabilities of data under H 0 H _0 , not probabilities of hypotheses.

## 16.3.7.3 Does it mean that if you ran the study again, you would obtain the same result 99% of the time?

No. The p-value is a statement about the likelihood of a particular dataset under the null; it does not allow us to make inferences about the likelihood of future events such as replication.

## 16.3.7.4 Does it mean that you have found a meaningful effect?

No. There is an important distinction between statistical significance and practical significance . As an example, let’s say that we performed a randomized controlled trial to examine the effect of a particular diet on body weight, and we find a statistically significant effect at p<.05. What this doesn’t tell us is how much weight was actually lost, which we refer to as the effect size (to be discussed in more detail in Chapter 18). If we think about a study of weight loss, then we probably don’t think that the loss of ten ounces (i.e. the weight of a bag of potato chips) is practically significant. Let’s look at our ability to detect a significant difference of 1 ounce as the sample size increases.

Figure 16.7 shows how the proportion of significant results increases as the sample size increases, such that with a very large sample size (about 262,000 total subjects), we will find a significant result in more than 90% of studies when there is a 1 ounce weight loss. While these are statistically significant, most physicians would not consider a weight loss of one ounce to be practically or clinically significant. We will explore this relationship in more detail when we return to the concept of statistical power in Section 18.3, but it should already be clear from this example that statistical significance is not necessarily indicative of practical significance.

## 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

## 12 Post Event Survey Questions to Ask

After your meticulously planned event concludes, there’s one crucial step left: gathering feedback. Post-event surveys are invaluable tools for understanding attendee experiences, identifying areas for improvement, and maintaining attendee satisfaction.

## Yes or No Questions in Online Surveys

This article will discuss the benefits of using yes or no questions, explore common examples, and provide practical tips for using them effectively in your surveys.

## How to Write Good Survey Questions

In this comprehensive guide, we will explore the impact of poorly designed survey questions, offer tips for crafting excellent and unbiased questionnaires, and provide examples of valid survey questions.

## 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.

In this blog post, we will explore the differences between Directional vs. Non-Directional Hypothesis in Research and their implications in research.

- 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.

The directional hypothesis is often used when there is prior knowledge or theoretical reasoning supporting the predicted direction of the relationship. It allows researchers to make more specific predictions and draw conclusions based on the expected direction of the effect.

## Example of Directional Hypothesis

For example, a directional hypothesis might state that “increased physical activity will lead to a decrease in body weight.” Here, the researcher expects a negative relationship between physical activity and body weight.

## Advantages of Directional Hypothesis

- Specific predictions: Directional hypotheses provide a clear prediction of the expected relationship between variables, allowing for a focused investigation.
- Increased statistical power: By focusing on one direction of the relationship, researchers can allocate more statistical power to that specific direction, increasing the chances of detecting a significant effect if it exists.

## Non-Directional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, does not make a specific prediction about the direction of the relationship between variables. Instead, it states that there is a relationship, but without indicating whether it will be positive or negative.

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 .

Read More: Internal Validity vs External Validity | Examples

## Example of Non-Directional Hypothesis

For example, a non-directional hypothesis might state that “there is a relationship between caffeine consumption and reaction time.” Here, the researcher expects a relationship between the variables but does not specify the direction.

Read More: Population vs Sample | Examples

## Advantages of Non-Directional Hypothesis:

- Flexibility: Non-directional hypotheses provide flexibility in exploring relationships between variables without preconceived notions about the direction of the effect.
- Open to unexpected findings : By not specifying the direction, researchers remain open to unexpected results or alternative explanations that may emerge during the analysis.

## 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:

- Prior research: If previous studies have established a clear direction of the relationship, a directional hypothesis may be more appropriate.
- Theoretical reasoning: If there is a strong theoretical foundation supporting a specific direction, a directional hypothesis can provide a focused investigation.
- Exploratory nature: If the research question is exploratory or lacks prior knowledge, a non-directional hypothesis allows for a more open-ended investigation.

Read More: Reliability vs Validity | Examples

- Directional vs. Non-Directional Hypothesis

Formulating hypotheses is an essential step in the research process, guiding researchers in testing relationships between variables.

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.

The choice between these types of hypotheses depends on the research question, prior knowledge, and theoretical background.

By understanding the distinctions between directional and non-directional hypotheses, researchers can effectively formulate hypotheses that align with their research goals and contribute to the advancement of scientific knowledge.

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

Please read through some of our other articles with examples and explanations if you’d like to learn more about research methodology.

Comparision

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- Friedman Test
- Chi-Square Test (Χ²)
- Effect Size

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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

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## Directional and nondirectional alternative hypotheses

- Published: 14 November 2013
- Volume 9 , pages 371–372, ( 1977 )

## Cite this article

- John Gaito 1

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The problem of one- vs two-tailed tests is discussed. The use of the terms “one tailed” and “two tailed” can lead to confusion, for example, with ANOVA procedures. It is suggested that these terms be replaced with “directional” and “nondirectional” alternative hypotheses. Furthermore, directional alternative hypotheses appear to contradict the operation of the null hypothesis. Thus, it appears that nondirectional alternative hypotheses should be used in all cases.

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Gaito, J. Directional and nondirectional alternative hypotheses. Bull. Psychon. Soc. 9 , 371–372 (1977). https://doi.org/10.3758/BF03337026

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Received : 30 December 1976

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A research hypothesis is a statement that predicts or expects a relationship between variables, and it is tested through research. To create a hypothesis, researchers often review existing literature on the topic. This hypothesis is based on theories, observations, or empirical evidence. It guides the research process, including experiment design, data collection, and analysis. Ultimately, the hypothesis aims to predict the outcome of the study.

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This article compares directional and non-directional hypotheses and provides guidelines for writing an effective hypothesis in research. The study explores the differences in predictions and research design implications between the two hypotheses.

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Types of hypothesis.

There are two main types of hypotheses in research:

## Null Hypothesis (H0)

The null hypothesis is the default assumption in statistical analysis that there is no significant relationship or effect between the variables being studied. It suggests that any observed differences or relationships are due to chance.

## Alternative Hypothesis (Ha or H1)

The alternative hypothesis proposes a significant relationship or effect between variables, contradicting the null hypothesis. It reflects the researcher's expectations based on existing theories or observations.

## What is Directional Hypotheses?

A directional hypothesis is a type of hypothesis that is used to predict a specific change or outcome in a research study. It is typically used when researchers have a clear idea of the direction in which they expect their results to go, either an increase or decrease, and want to test this prediction. By making a directional hypothesis, researchers can focus their research efforts and design studies that are more likely to uncover meaningful results. In essence, a directional hypothesis is a statement that predicts the direction of the change that is expected to occur between two groups or variables that are being investigated.

## Examples of Directional Hypothesis

Example 1: Online versus Traditional Classroom Learning

For instance, consider a study comparing the average study time of college students in online courses versus those in traditional classroom settings. Drawing on prior research indicating that online learning might lead to reduced engagement, a potential directional hypothesis could be: "Students enrolled in online classes will spend fewer weekly study hours than those in traditional classrooms."

In this scenario, our hypothesis presents a clear expectation—that the average number of weekly study hours among online learners will be lower than that of traditional learners. If the actual findings reveal no significant difference or even higher study times among online learners, then our hypothesis would be refuted.

Example 2: Carbon Dioxide Levels and Global Warming

A directional hypothesis in this scenario would propose a specific change in direction between these two variables. For instance, a directional hypothesis might state that as carbon dioxide levels increase, global temperatures will also rise. This hypothesis suggests a causal relationship between the increase in CO2 levels and the phenomenon of global warming, indicating a direction of change in global temperatures corresponding to changes in CO2 levels.

## What is a Non-Directional Hypotheses?

In scientific research, a non-directional hypothesis, or null hypothesis, is a statement that suggests the absence of a relationship or difference between the variables being studied. This type of hypothesis is used to test the validity of a research question by assuming that there is no significant effect or relationship between the variables under investigation. The null hypothesis is typically tested against an alternative hypothesis, which proposes that there is a significant effect or relationship between the variables. If the null hypothesis is rejected, it means that there is enough evidence to suggest that the alternative hypothesis is true, and the variables are indeed related or different from each other.

## Non-Directional Hypothesis Example

Example: Is there a difference in anxiety levels between students who receive traditional classroom instruction and those who participate in online learning?

In this non-directional hypothesis, researchers are interested in understanding if there's a disparity in anxiety levels between students who are taught in traditional classrooms versus those who learn online. The non-directional hypothesis posits that there won't be any notable variance in anxiety levels between the two groups. This means that the researchers are not predicting whether one group will have higher or lower anxiety levels; rather, they are exploring if there's any difference at all.

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## Directional vs. Non-Directional Hypotheses in Research

Both directional and non directional hypothesis have their place in research, and choosing the appropriate type depends on the research question being investigated. Researchers can use directional or non-directional hypotheses in their studies, depending on their specific expectations about the relationship between variables. A directional hypothesis predicts a specific direction of change, while a non-directional hypothesis predicts that there will be a difference between groups or conditions without specifying the direction of that difference. It's important to understand the difference between these types of hypotheses to conduct rigorous and insightful research. Directional hypotheses are useful when researchers want to test a specific expectation about the relationship between variables, while non-directional hypotheses are more appropriate when researchers simply want to test if there is any difference between groups or conditions.

## How to Write an Effective Hypothesis in Research?

Writing an effective hypothesis involves several key steps to ensure clarity, testability, and relevance to the research question. Here's a guide on how to write an effective hypothesis:

- Identify the Research Question: Start by clearly defining the research question or problem you want to investigate. Your hypothesis should directly address this question.
- State the Null Hypothesis: The null hypothesis (H0) is a statement that there is no relationship or effect between the variables being studied. It serves as the default assumption and is typically stated as the absence of an effect or difference.
- Formulate the Alternative Hypothesis: The alternative hypothesis (H1 or Ha) is the statement that contradicts the null hypothesis and suggests that there is a relationship or effect between the variables. It reflects what you expect to find in your research.
- Make it Testable: Your hypothesis should be testable through empirical observation or experimentation. This means that there must be a way to collect data or evidence to support or refute the hypothesis.
- Be Specific and Clear: Clearly state the variables involved and the expected relationship between them. Avoid vague or ambiguous language to ensure that your hypothesis is easy to understand and interpret.
- Use Quantifiable Terms: Whenever possible, use quantifiable terms or measurable variables in your hypothesis. This makes it easier to collect data and analyze results objectively.
- Consider the Scope: Ensure that your hypothesis is focused and specific to the research hypothesis at hand. Avoid making broad generalizations that are difficult to test or validate.
- Revise and Refine: Once you've drafted your hypothesis, review it carefully to ensure accuracy and coherence. Revise as needed to clarify any ambiguities or inconsistencies.

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In conclusion, directional hypotheses predict whether variables will increase or decrease, providing a definite expectation about the direction of the relationship under investigation. Non-directional hypotheses, on the other hand, only claim that there is a difference between variables without specifying the direction of the change, leaving it open to any possibility. Both types of hypotheses play an important role in guiding research investigations and developing testable predictions.

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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 ...

Whenever we perform a hypothesis test, we always write down a null and alternative hypothesis: Null Hypothesis (H 0): The sample data occurs purely from chance. Alternative Hypothesis (H A): The sample data is influenced by some non-random cause. A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis ...

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 ...

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. ... 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 ...

5.2 - Writing Hypotheses. The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis ( H 0) and an alternative hypothesis ( H a ). When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the ...

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 ( H0) answers "No, there's no effect in the population.". The alternative hypothesis ( Ha) answers "Yes, there is an effect in the ...

One example is where water quality in a stream has been observed over many years, and a test is made of the null hypothesis that "there is no change in quality between the first and second halves of the data", against the alternative hypothesis that "the quality is poorer in the second half of the record". ... A non-directional alternative ...

Thus, our alternative hypothesis is the mathematical way of stating our research question. If we expect our obtained sample mean to be above or below the null hypothesis value, which we call a directional hypothesis, then our alternative hypothesis takes the form: HA: μ > 7.47 or HA: μ < 7.47 H A: μ > 7.47 or H A: μ < 7.47.

Examples of research questions for directional hypothesis. 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.

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 ...

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. The null and alternative hypotheses in this case would be: Null hypothesis: µ = 70 inches.

A Level Psychology Topic Quiz - Research Methods. Quizzes & Activities. 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).

Tips for Writing a Non-Directional Hypothesis. Start with Inquiry: Frame your hypothesis as an answer to a research question. Embrace Openness: Non-directional hypotheses are ideal when no strong expectation exists. Be Succinct: Keep the hypothesis statement concise and clear.

and the corresponding non-directional alternative hypothesis would be: H A: B M I a c t i v e ≠ B M I i n a c t i v e HA: BMI_{active} \neq BMI_{inactive} A directional hypothesis, on the other hand, predicts which direction the difference would go. For example, we have strong prior knowledge to predict that people who engage in physical ...

On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies (non-directional hypothesis).4 In addition, hypotheses can 1) define interdependency between variables (associative hypothesis),4 2) propose an effect on the dependent variable from manipulation ...

This includes any hypothesis that predicts positive correlation, negative correlation, non-directional correlation or causation. The only hypothesis that isn't an alternative hypothesis is a null hypothesis that predicts no relationship between independent and dependent variables. The following are hypothetical examples of an alternative ...

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.

Advantages of Non-Directional Hypothesis: Flexibility: Non-directional hypotheses provide flexibility in exploring relationships between variables without preconceived notions about the direction of the effect. Open to unexpected findings: By not specifying the direction, researchers remain open to unexpected results or alternative explanations that may emerge during the analysis.

In a two-tailed test, the generic null and alternative hypotheses are the following: Null: The effect equals zero. Alternative: The effect does not equal zero. The specifics of the hypotheses depend on the type of test you perform because you might be assessing means, proportions, or rates. Example of a two-tailed 1-sample t-test

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 problem of one- vs two-tailed tests is discussed. The use of the terms "one tailed" and "two tailed" can lead to confusion, for example, with ANOVA procedures. It is suggested that these terms be replaced with "directional" and "nondirectional" alternative hypotheses. Furthermore, directional alternative hypotheses appear to contradict the operation of the null hypothesis ...

Non-Directional Hypothesis Example. Example: Is there a difference in anxiety levels between students who receive traditional classroom instruction and those who participate in online learning? ... Formulate the Alternative Hypothesis: The alternative hypothesis (H1 or Ha) is the statement that contradicts the null hypothesis and suggests that ...

For example, a researcher might hypothesize that college students will perform differently from elementary school students on a memory task without predicting which group of students will perform better. Also called nondirectional alternative hypothesis; two-tailed (alternative) hypothesis. Compare directional hypothesis.