directional hypothesis wikipedia

Statistics Made Easy

What is a Directional Hypothesis? (Definition & Examples)

A statistical hypothesis is an assumption about a population parameter . For example, we may assume that the mean height of a male in the U.S. is 70 inches.

The assumption about the height is the statistical hypothesis and the true mean height of a male in the U.S. is the population parameter .

To test whether a statistical hypothesis about a population parameter is true, we obtain a random sample from the population and perform a hypothesis test on the sample data.

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: The alternative hypothesis contains the less than (“<“) or greater than (“>”) sign. This indicates that we’re testing whether or not there is a positive or negative effect.
Non-directional hypothesis: The alternative hypothesis contains the not equal (“≠”) sign. This indicates that we’re testing whether or not there is some effect, without specifying the direction of the effect.

Note that directional hypothesis tests are also called “one-tailed” tests and non-directional hypothesis tests are also called “two-tailed” tests.

Check out the following examples to gain a better understanding of directional vs. non-directional hypothesis tests.

Example 1: Baseball Programs

A baseball coach believes a certain 4-week program will increase the mean hitting percentage of his players, which is currently 0.285.

To test this, he measures the hitting percentage of each of his players before and after participating in the program.

He then performs a hypothesis test using the following hypotheses:

H 0 : μ = .285 (the program will have no effect on the mean hitting percentage)
H A : μ > .285 (the program will cause mean hitting percentage to increase)

This is an example of a directional hypothesis because the alternative hypothesis contains the greater than “>” sign. The coach believes that the program will influence the mean hitting percentage of his players in a positive direction.

Example 2: Plant Growth

A biologist believes that a certain pesticide will cause plants to grow less during a one-month period than they normally do, which is currently 10 inches.

To test this, she applies the pesticide to each of the plants in her laboratory for one month.

She then performs a hypothesis test using the following hypotheses:

H 0 : μ = 10 inches (the pesticide will have no effect on the mean plant growth)
H A : μ < 10 inches (the pesticide will cause mean plant growth to decrease)

This is also an example of a directional hypothesis because the alternative hypothesis contains the less than “<” sign. The biologist believes that the pesticide will influence the mean plant growth in a negative direction.

Example 3: Studying Technique

A professor believes that a certain studying technique will influence the mean score that her students receive on a certain exam, but she’s unsure if it will increase or decrease the mean score, which is currently 82.

To test this, she lets each student use the studying technique for one month leading up to the exam and then administers the same exam to each of the students.

H 0 : μ = 82 (the studying technique will have no effect on the mean exam score)
H A : μ ≠ 82 (the studying technique will cause the mean exam score to be different than 82)

This is an example of a non-directional hypothesis because the alternative hypothesis contains the not equal “≠” sign. The professor believes that the studying technique will influence the mean exam score, but doesn’t specify whether it will cause the mean score to increase or decrease.

Additional Resources

Introduction to Hypothesis Testing Introduction to the One Sample t-test Introduction to the Two Sample t-test Introduction to the Paired Samples t-test

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Directional Hypothesis: Definition and 10 Examples

directional hypothesis examples and definition, explained below

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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Introduction to Psychology/Research Methods in Psychology

RESEARCH METHOD IN PSYCHOLOGY.

Research psychology encompasses the study of behavior for use in academic settings, and contains numerous areas. It contains the areas of abnormal psychology, biological psychology, cognitive psychology, comparative psychology, developmental psychology, personality psychology, social psychology and others. All branches of psychology can have a research component to them. Research psychology is contrasted with applied psychology.

Research in psychology is conducted in broad accord with the standards of the scientific method, encompassing both qualitative ethological and quantitative statistical modalities to generate and evaluate explanatory hypotheses with regard to psychological phenomena. Where research ethics and the state of development in a given research domain permits, investigation may be pursued by experimental protocols. Psychology tends to be eclectic, drawing on scientific knowledge from other fields to help explain and understand psychological phenomena. Qualitative psychological research utilizes a broad spectrum of observational methods, including action research, ethography, exploratory statistics, structured interviews, and participant observation, to enable the gathering of rich information unattainable by classical experimentation. Research in humanistic psychology is more typically pursued by ethnographic, historical, and historiographic methods.

The testing of different aspects of psychological function is a significant area of contemporary psychology. Psychometric and statistical methods predominate, including various well-known standardized tests as well as those created ad hoc as the situation or experiment requires.

Academic psychologists may focus purely on research and psychological theory, aiming to further psychological understanding in a particular area, while other psychologists may work in applied psychology to deploy such knowledge for immediate and practical benefit. However, these approaches are not mutually exclusive and most psychologists will be involved in both researching and applying psychology at some point during their career. Clinical psychology, among many of the various disciplines of psychology, aims at developing in practicing psychologists knowledge of and experience with research and experimental methods which they will continue to build up as well as employ as they treat individuals with psychological issues or use psychology to help others.

When an area of interest requires specific training and specialist knowledge, especially in applied areas, psychological associations normally establish a governing body to manage training requirements. Similarly, requirements may be laid down for university degrees in psychology, so that students acquire an adequate knowledge in a number of areas. Additionally, areas of practical psychology, where psychologists offer treatment to others, may require that psychologists be licensed by government regulatory bodies as well.

Quantitative psychology involves the application of statistical analysis to psychological research, and the development of novel statistical approaches for measuring and explaining human behavior. It is a young field (only recently have Ph.D. programs in quantitative psychology been formed), and it is loosely comprised of the subfields psychometrics and mathematical psychology.

Psychometrics is the field of psychology concerned with the theory and technique of psychological measurement, which includes the measurement of knowledge, abilities, attitudes, interests, achievement in particular degree or course, and personality traits (Carl Dellomos, 2009). Measurement of these unobservable phenomena is difficult, and much of the research and accumulated knowledge in this discipline has been developed in an attempt to properly define and quantify such phenomena. Psychometric research typically involves two major research tasks, namely: (i) the construction of instruments and procedures for measurement; and (ii) the development and refinement of theoretical approaches to measurement.

Psychology is a science, to be approached as such. Experiments should be designed using the scientific method.

There are several research methods that psychologists employ:

1.1 Nomothetic (Quantitative Approach)
1.2 Idiographic (Qualitative Approach)
2.1 Descriptive Studies
2.2 Factorial Design
2.3 Correlational Study
2.4 Experiments
2.5 Naturalistic Observation
2.6 Self Report
3.1.1 Statistical Symbols
3.1.2 Frequency Distribution
3.1.3 Measures of Central Tendency
3.1.4 Measures of Variability
3.2 Case Studies
4 Basic Concepts

Approaches in Psychology Research [ edit | edit source ]

Nomothetic (quantitative approach) [ edit | edit source ].

This approach is basically used in inferential and descriptive statistics as both mediums of scientific method of investigation in analyzing, presenting, and interpretation of data gathered by the researcher through standardized or objective instruments (e.g. psychological Tests). Nomothetic can also be defined as data that is collected from a specifically defined number of individuals (sample) to measure certain tendencies while making hypothetical predictions about human behaviors (population). The term “nomothetic” comes from the Greek word “nomos” meaning “law”. Psychologists who adopt this approach are mainly concerned with studying what we share with others. That is to say in establishing laws or generalizations. (Carl Dellomos, 2009)

Idiographic (Qualitative Approach) [ edit | edit source ]

This approach tends not to use inferential or descriptive statistics, but rather uses qualitative methods of data gathering such as interviews, diaries, and other written materials, obtained from or provided by the expected or anticipated respondents of a particular research. The term “idiographic” comes from the Greek word “idios” meaning “own” or “private”. Psychologists interested in this aspect of experience seek to discover what makes each of us unique. Despite the importance of our genetic individuality, proceeding from biology, the distinction between the nomothetic and the idiographic is often equated with two types of science — the natural sciences concerned with discovering laws of nature, and the social sciences concerned with individual meanings. We can examine these differences further by seeing how they relate to personality theory.(Carl Dellomos, 2009)

Both approaches were introduced by Gordon Allport. (Carl Dellomos, 2009)

Research Designs [ edit | edit source ]

Research design is the way in which the overall study is ran, this goes for the way the data is collected, measured, and interpreted. Considered as the backbone of an experiment as it sets the overall presence for how things will flow throughout duration of the study. There are four main types of research designs that are used within the psychology field: descriptive or qualitative, correlational, casual comparative/ quasi-experimental, and experimental. The method of data collection also varies, with self-report on one end of the spectrum, and naturalistic observation on the other.

Descriptive Studies [ edit | edit source ]

The Studies that do not test specific relationships between variables are called descriptive studies. In this research method, general or specific behaviors or attributes are observed and measured, without respect to each other. These studies are generally the design of choice for breaking into new areas, as the vast but often inconclusive amount of information collected can be drawn upon for future hypotheses.

An example of such a study would be a researcher inquiring into the quality of mental health institutions. This would be done by observation or measurements of various criteria, as opposed to relationships between variables. Alternatively, the study could be conducted without any specific criteria in mind.

Factorial Design [ edit | edit source ]

Every level of one independent variable is joined with every level of the others in a factorial design to create every possible combination. Because this particular design contains two variables, each of which has two levels, it is known as a 2 × 2 (or "two-by-two") factorial design. There would be six different conditions in a 3 × 2 factorial design if one of the independent variables had a third level. Observe that the product of the levels' numbers is the total number of possible circumstances. There are four conditions in a 2 × 2 factorial design, six in a 3 × 2 factorial design, twenty in a 4 × 5 factorial design, and so on. It's also important to note that every number in the notation indicates a single independent variable or component. Because this particular design contains two variables, each of which has two levels, it is known as a 2 × 2 (or "two-by-two") factorial design. There would be six different conditions in a 3 × 2 factorial design if one of the independent variables had a third level. Observe that the product of the levels' numbers is the total number of possible circumstances. There are four conditions in a 2 × 2 factorial design, six in a 3 × 2 factorial design, twenty in a 4 × 5 factorial design, and so on. It's also important to note that every number in the notation indicates a single independent variable or component.

Correlational Study [ edit | edit source ]

This method of statistical analysis shows the relationship between two variables. For example, research has shown that alcohol dependence correlates with depression. That is to say, the more alcohol people consume, the more depressed they become. On the other hand, it could be the other way around as well: the more depressed people become, the more likely they are to consume alcohol.

The attributes of correlations include strength and direction. The direction may be positive (both variables both increase or decrease together), negative (one variable increases while the other decreases) or unrelated (a random relationship between variables). The strength of a correlation ranges from -1 to +1 with a 0 reflecting no relationship between variables. A correlational study serves only to describe/predict behavior and not to explain it. This is so because a third variable could be shown to cause the occurrence of one of the variables. Furthermore, only experiments can prove causation.

Experiments [ edit | edit source ]

Experiments are generally the studies that are the most precise and have the most weight to them due to their conclusive power. They are particularly effective in proving hypotheses about cause and effect relationships between variables. A hypothesis is a prediction of how one variable relates to another. There are two types of hypotheses, null and directional . The null is a prediction that there will not be any change in the dependent variable when the researcher changes the independent variable. The directional hypothesis states that the change in the independent variable will induce a change in the dependent variable. In a true experiment, all variables are held constant except for the independent variable, which is manipulated. Thus, any changes in the experimental groups can be solely attributed to the action of the independent variable. This is called being objective .

For instance, in an experiment to test whether music improves people's memories, we would have a sheet of paper with ten unrelated words on it for people to memorize. The control group would have no music playing in the background while the experimental group would have some music in the background. Because as researchers we have adhered to the scientific method and held all variables as constant as possible, if the experimental group does report better recollection of words, then we could assume that the music had an effect on memory. However, we must be certain to do our best to ensure that any controllable differences between the two groups are eliminated in order to ensure that no confounding variable interferes with the experiment.

There are two main ways to pick, or sample the subjects in an experiment, random and stratified . In a random sampling each person has an equal chance at being picked. This means that if 80% of the population being sampled from are Christian, then 80% of the sample will be Christian. If the researcher wanted all religions represented equally, he would employ stratified sampling. For instance, the experiment could be performed only on women, or on mixed groups with equal numbers of each sex in them, to eliminate the possibility of biased results from one gender having better average memory than the other.

Steps must be taken to make sure that there is no experimenter bias . Two common forms of bias are demand characteristics and expectancy effects. If a researcher expects certain results from an experiment and influences the subjects response this is called demand characteristics. If the experimenter inadvertently interprets the information to be as expected in their hypothesis it is called expectancy effect. To counteract experimenter bias the subjects can be kept uninformed on the intentions of the experiment, which is called single blind. If the people collecting the information and the subjects giving it are kept uninformed then it is called a double blind experiment.

The experiment should also be reported so that other researchers can repeat it. If an experiment isn't repeatable it will not hold much weight in the scientific community. To help an experiment be repeatable the researcher should have the variables be measureable, this is called being empirical .

Whether researching humans or animals the experiment should be ethical. When humans are the subjects they should be informed of what the study is, consent to being in it, be debriefed afterwards, and their information should also be kept confidential .

Naturalistic Observation [ edit | edit source ]

Researchers study organisms in their natural environments or habitats without trying to manipulate or control anything. In this method, the researcher observes the behavior under study in its natural setting while attempting to avoid influencing or controlling it. The observations are done in a naturalistic setting without any preparation or participation of the researcher. Therefore, the behavior is observed in public places, streets, homes, and schools. Observing people from other cultures response in the same setting is a way to provide information for cross-cultural research .

Self Report [ edit | edit source ]

This method includes tests , questionnaires , and interviews . All of which do the same thing, give the subject a stimuli, i.e. the question, and get a response. The advantage of using these is the ability to inexpensively and rapidly collect vast amounts of data. This allows a psychologist to compare one person, or a group of peoples results to thousands of others. The disadvantage is that they are not always telling what the subject's response is but what the subject says is the response.

Information Display [ edit | edit source ]

Statistics [ edit | edit source ].

Once the information is gathered it has to be put into some kind of form, usually numerical. Statistics deals with the collection, analysis, interpretation, and presentation of numerical data. The goal of statistics is to summarize the data and let descriptions or inferences be made . Inferences are used when making predictions of the relationships of variables. Descriptions are concise displays, using statistical symbols ,of the information in frequency distributions, measures of central tendency , or measures of variability .

Statistical Symbols [ edit | edit source ]

There are agreed upon standard symbols used in statistical displays. These symbols can be used by themselves or in equations.

N = number of scores

X = score (or scores)

d = difference of a score from the mean

D = difference in rank

r or ρ = correlation

SD = standard deviation

Frequency Distribution [ edit | edit source ]

A frequency distribution is obtained by taking the score and splitting them into subgroups. The subgroups are then put on either a histogram (bar graph) or a frequency polygram (line graph). When a frequency distribution has most of the scores on one side of the graph it is considered skewed. If it has most of the scores in the middle with equal amounts on both sides it is considered symmetrical.

Measures of Central Tendency [ edit | edit source ]

In measures of central tendency there is one number that is used to represent a group of numbers. This number is either the mean , median , or the mode .

Measures of Variability [ edit | edit source ]

Variability is concerned with the dispersal of the scores, called variability i.e. are the scores clustered together or spread out. Range and standard deviation are the measures most commonly used. To find the range just subtract the number of the lowest score from the number of the highest score. This can be deceiving if most of the scores are bunched together and one of the scores is very far away from it. In this case standard deviation must be used. A formula commonly used for standard deviation is SD = the square root of Σd²/N.

Case Studies [ edit | edit source ]

In the course of treating a patient a psychologist will take records of problems, insights, and techniques that were important in the patients treatment. A clinical case history may be drawn upon by researchers to expose a factor that is important for understanding a behavior. Case studies repeat and are used as guides for psychologists.

Basic Concepts [ edit | edit source ]

a. independent variable = variable that one manipulates in order to see if it has any effect on the dependent variable (eg. in the example above, the independent variable would be music and its effect on memory)
b. dependent variable = variable that depends on the effect of the independent variable (e.g. in the example above, the dependent variable would be memory and better recollection of words)
c. double-blind procedure = procedure in which neither the researcher nor the subjects know which group (experimental or control) the subjects are in in order to minimize experimenter cues.
d. single-blind procedure = procedure in which only the researcher knows which group which kind of subject is in.
e. experimenter cues - subtle and often unintentional cues that the experimenter makes which implies which group which kind of subject is in. for example, if an experimenter believes that music does indeed improve memory, some cues would be the experimenter's smiling/winking at the experimental group. This smile/wink would imply to the subjects in the experimental group that the researcher is secretly implying that they're in the experimental group.
f. placebo effect - a treatment works because of the patient's belief that it works and not because it actually does.
g. experimenter - the person who is researching through the participant.

Book:Introduction to Psychology

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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The What, Why and How of Directional Hypotheses

In the world of research and science, hypotheses serve as the starting blocks, setting the pace for the entire study. One such hypothesis type is the directional hypothesis. Here, we delve into what exactly a directional hypothesis is, its significance, and the nitty-gritty of formulating one, followed by pitfalls to avoid and how to apply it in practical situations.

The What: Understanding the Concept of a Directional Hypothesis

A directional hypothesis, often referred to as a one-tailed hypothesis, is an essential part of research that predicts the expected outcomes and their directions. The intriguing aspect here is that it goes beyond merely predicting a difference or connection, it actually suggests the direction that this difference or connection will take.

Let's break it down a bit. If the directional hypothesis is positive, this suggests that the variables being studied are expected to either increase or decrease in unison. On the other hand, if the hypothesis is negative, it implies that the variables will move in opposite directions - as one variable ascends, the other will descend, and vice versa.

This intricacy gives the directional hypothesis its unique value in research and offers a fascinating aspect of study predictions. With a clearer understanding of what a directional hypothesis is, we can now delve into why it holds such significance in research and how to construct one effectively.

The Why: The Significance of a Directional Hypothesis in Research

Ever wondered why the directional hypothesis is held in such high regard? The secret lies in its unique blend of precision and specificity. It provides an edge by paving the way for a more concentrated and focused investigation. Essentially, it helps scientists to have an informed prediction of the correlation between variables, underpinned by prior research, theoretical assumptions, or logical reasoning. This isn't just a game of guesswork but a highly credible route to more definitive and dependable results. As they say, the devil is in the detail. By using a directional hypothesis, we are able to dive into the intricate and exciting world of research, adding a robust foundation to our endeavours, ultimately boosting the credibility and reliability of our findings. By standing firmly on the shoulders of the directional hypothesis, we allow our research to gaze further and see clearer.

The How: Constructing a Strong Directional Hypothesis

Crafting a robust directional hypothesis is indeed a craft that requires a blend of art and science. This process starts with a comprehensive exploration of related literature, immersing oneself in the reservoir of knowledge that already exists around your subject of interest. This immersion enables you to soak up invaluable insights, creating a well-informed base from which to make educated predictions about the directionality between your variables of interest.

The process doesn't stop at a literature review. It's also imperative to fully comprehend your subject. Dive deeper into the layers of your topic, unpick the threads, and question the status quo. Understand what drives your variables, how they may interact, and why you anticipate they'll behave in a certain way.

Then, it's time to define your variables clearly and precisely. This might sound simple, but it's crucial to be as accurate as possible. By doing so, you not only ensure a clear understanding of what you are measuring, but you also set clear parameters for your research.

Following that, comes the exciting part - predicting the direction of the relationship between your variables. This prediction should not be a wild guess, but an informed forecast grounded in your literature review, understanding of the subject, and clear definition of variables.

Finally, remember that a directional hypothesis is not set in stone. It is, by definition, a hypothesis - a proposed explanation or prediction that is subject to testing and verification. So, don’t be disheartened if your directional hypothesis doesn’t pan out as expected. Instead, see it as an opportunity to delve further, learn more and further the boundaries of knowledge in your field. After all, research is not just about confirming hypotheses, but also about the thrill of exploration, discovery, and ultimately, growth.

Pitfalls to Avoid When Formulating a Directional Hypothesis

Crafting a directional hypothesis isn't a walk in the park. A few common missteps can muddy the waters and limit the effectiveness of your hypothesis. The first stumbling block that researchers should watch out for is making baseless presumptions. Although predicting the course of the relationship between variables is integral to a directional hypothesis, this prediction should be firmly rooted in evidence, not just whims or gut feelings.

Secondly, steer clear of being excessively rigid with your hypothesis. Remember, it's a guide, not gospel truth. Science is about exploration, about finding out, about being open to unexpected outcomes. If your hypothesis does not match the results, that's not failure; it's a chance to learn and expand your understanding.

Avoid creating an overly complex hypothesis. Simplicity is the name of the game. You want your hypothesis to be clear, concise, and comprehensible, not wrapped in jargon and unnecessary complexities.

Lastly, ensure that your directional hypothesis is testable. It's not enough to merely state a prediction; it needs to be something you can verify empirically. If it can't be tested, it's not a viable hypothesis. So, when creating your directional hypothesis, be mindful to keep it within the realm of testable claims.

Remember, falling into these traps can derail your research and limit the value of your findings. By keeping these pitfalls at bay, you are better equipped to navigate the fascinating labyrinth of research, while contributing to a deeper understanding of your field. Happy hypothesising!

Putting it All Together: Applying a Directional Hypothesis in Practice

When it comes to applying a directional hypothesis, the real fun begins as you put your prediction to the test using appropriate research methodologies and statistical techniques. Let's put this into perspective using an example. Suppose you're exploring the effect of physical activity on people's mood. Your directional hypothesis might suggest that engaging in exercise would result in an improvement in mood ratings.

To test this hypothesis, you could employ a repeated-measures design. Here, you measure the moods of your participants before they start the exercise routine and then again after they've completed it. If the data reveals an uplift in positive mood ratings post-exercise, you would have empirical evidence to support your directional hypothesis.

However, bear in mind that your findings might not always corroborate your prediction. And that's the beauty of research! Contradictory findings don't necessarily signify failure. Instead, they open up new avenues of inquiry, challenging us to refine our understanding and fuel our intellectual curiosity. Therefore, whether your directional hypothesis is proven correct or not, it still serves a valuable purpose by guiding your exploration and contributing to the ever-evolving body of knowledge in your field. So, go ahead and plunge into the exciting world of research with your well-crafted directional hypothesis, ready to embrace whatever comes your way with open arms. Happy researching!

Directional Hypothesis

Definition:

A directional hypothesis is a specific type of hypothesis statement in which the researcher predicts the direction or effect of the relationship between two variables.

Key Features

1. Predicts direction:

Unlike a non-directional hypothesis, which simply states that there is a relationship between two variables, a directional hypothesis specifies the expected direction of the relationship.

2. Involves one-tailed test:

Directional hypotheses typically require a one-tailed statistical test, as they are concerned with whether the relationship is positive or negative, rather than simply whether a relationship exists.

3. Example:

An example of a directional hypothesis would be: “Increasing levels of exercise will result in greater weight loss.”

4. Researcher’s prior belief:

A directional hypothesis is often formed based on the researcher’s prior knowledge, theoretical understanding, or previous empirical evidence relating to the variables under investigation.

5. Confirmatory nature:

Directional hypotheses are considered confirmatory, as they provide a specific prediction that can be tested statistically, allowing researchers to either support or reject the hypothesis.

6. Advantages and disadvantages:

Directional hypotheses help focus the research by explicitly stating the expected relationship, but they can also limit exploration of alternative explanations or unexpected findings.

How to Write a Directional Hypothesis: A Step-by-Step Guide

In research, hypotheses play a crucial role in guiding investigations and making predictions about relationships between variables.

One type of hypothesis that researchers often encounter is the directional hypothesis, also known as a one-tailed hypothesis.

In this blog post, we’ll explore what a directional hypothesis is, why it’s important, and provide a step-by-step guide on how to write one effectively.

Table of Contents

What is a Directional Hypothesis?

A directional hypothesis is a statement that predicts the direction of the relationship between two variables. Unlike non-directional hypotheses, which simply state that there is a relationship between variables without specifying the direction, directional hypotheses make a clear prediction about the expected outcome.

For example, a directional hypothesis might predict that an increase in one variable will lead to a decrease in another.

Examples of Directional Hypotheses

Increasing the amount of sunlight exposure will lead to higher levels of vitamin D in the body.
Decreasing the amount of sugar consumption will result in lower body weight among participants.
Introducing mindfulness meditation techniques will reduce symptoms of anxiety in patients with generalized anxiety disorder.

Why to Write a Directional Hypothesis?

Directional hypotheses offer several advantages in research. They provide researchers with a more focused prediction, allowing them to test specific hypotheses rather than exploring all possible relationships between variables.

This can help streamline research efforts and increase the likelihood of finding meaningful results. Additionally, directional hypotheses are often used in experimental research, where researchers manipulate variables to observe their effects on outcomes.

Step 1: Identify the Variables

Start by identifying the independent variable (the variable you are manipulating) and the dependent variable (the variable you are measuring). Understanding the relationship between these variables is essential for writing a directional hypothesis.

Step 2: Predict the Direction

Based on your understanding of the relationship between the variables, predict the direction of the effect.

Will an increase in the independent variable lead to an increase or decrease in the dependent variable?

Be specific in your prediction.

Step 3: Use Clear Language

Write your directional hypothesis using clear and concise language. Avoid technical jargon or terms that may be difficult for readers to understand. Your hypothesis should be easily understood by both researchers and non-experts.

Step 4: Ensure Testability

Ensure that your hypothesis is testable by collecting data and conducting statistical analysis. You should be able to measure the variables and determine whether the observed results support or refute your hypothesis.

Step 5: Revise and Refine

Review your directional hypothesis to ensure that it accurately reflects your research question and predictions. Make any necessary revisions to improve clarity and specificity.

Writing a directional hypothesis is an essential skill for researchers conducting experiments and investigations.

By following the steps outlined in this guide, you can effectively formulate hypotheses that make clear predictions about the relationship between variables.

Whether you’re a researcher or just starting out in the field, mastering the art of writing directional hypotheses will enhance the quality and rigor of your research endeavors.

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What is a Directional Hypothesis? (Definition & Examples)

A statistical hypothesis is an assumption about a population parameter . For example, we may assume that the mean height of a male in the U.S. is 70 inches.

The assumption about the height is the statistical hypothesis and the true mean height of a male in the U.S. is the population parameter .

To test whether a statistical hypothesis about a population parameter is true, we obtain a random sample from the population and perform a hypothesis test on the sample data.

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: The alternative hypothesis contains the less than (“”) sign. This indicates that we’re testing whether or not there is a positive or negative effect.
Non-directional hypothesis: The alternative hypothesis contains the not equal (“≠”) sign. This indicates that we’re testing whether or not there is some effect, without specifying the direction of the effect.

Note that directional hypothesis tests are also called “one-tailed” tests and non-directional hypothesis tests are also called “two-tailed” tests.

Check out the following examples to gain a better understanding of directional vs. non-directional hypothesis tests.

Example 1: Baseball Programs

A baseball coach believes a certain 4-week program will increase the mean hitting percentage of his players, which is currently 0.285.

To test this, he measures the hitting percentage of each of his players before and after participating in the program.

He then performs a hypothesis test using the following hypotheses:

H 0 : μ = .285 (the program will have no effect on the mean hitting percentage)
H A : μ > .285 (the program will cause mean hitting percentage to increase)

Example 2: Plant Growth

A biologist believes that a certain pesticide will cause plants to grow less during a one-month period than they normally do, which is currently 10 inches.

To test this, she applies the pesticide to each of the plants in her laboratory for one month.

She then performs a hypothesis test using the following hypotheses:

H 0 : μ = 10 inches (the pesticide will have no effect on the mean plant growth)

This is also an example of a directional hypothesis because the alternative hypothesis contains the less than “negative direction.

Example 3: Studying Technique

To test this, she lets each student use the studying technique for one month leading up to the exam and then administers the same exam to each of the students.

H 0 : μ = 82 (the studying technique will have no effect on the mean exam score)
H A : μ ≠ 82 (the studying technique will cause the mean exam score to be different than 82)

Additional Resources

Introduction to Hypothesis Testing Introduction to the One Sample t-test Introduction to the Two Sample t-test Introduction to the Paired Samples t-test

How to Perform a Partial F-Test in Excel

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Directional Hypothesis

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).

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8.4: The Alternative Hypothesis

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Foster et al.
University of Missouri-St. Louis, Rice University, & University of Houston, Downtown Campus via University of Missouri’s Affordable and Open Access Educational Resources Initiative

If the null hypothesis is rejected, then we will need some other explanation, which we call the alternative hypothesis, \(H_A\) or \(H_1\). The alternative hypothesis is simply the reverse of the null hypothesis, and there are three options, depending on where we expect the difference to lie. 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:

\[\mathrm{H}_{\mathrm{A}}: \mu>7.47 \quad \text { or } \quad \mathrm{H}_{\mathrm{A}}: \mu<7.47 \nonumber \]

based on the research question itself. We should only use a directional hypothesis if we have good reason, based on prior observations or research, to suspect a particular direction. When we do not know the direction, such as when we are entering a new area of research, we use a non-directional alternative:

\[\mathrm{H}_{\mathrm{A}}: \mu \neq 7.47 \nonumber \]

We will set different criteria for rejecting the null hypothesis based on the directionality (greater than, less than, or not equal to) of the alternative. To understand why, we need to see where our criteria come from and how they relate to \(z\)-scores and distributions.

Writing hypotheses in words

As we alluded to in the null hypothesis section, we can write our hypotheses in word statements (in addition to the statements with symbols). These statements should be specific enough to the particular experiment or situation being referred to. That is, don't make them generic enough so that they would apply to any hypothesis test that you would conduct.

Examples for how to write null and alternate hypotheses in words for directional and non-directional situations are given throughout the chapters.

Contributors and Attributions

Foster et al. (University of Missouri-St. Louis, Rice University, & University of Houston, Downtown Campus)

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

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Bi-directional hypothesis of language and action

The bi-directional hypothesis of language and action proposes that the sensorimotor and language comprehension areas of the brain exert reciprocal influence over one another. [1] This hypothesis argues that areas of the brain involved in movement and sensation, as well as movement itself, influence cognitive processes such as language comprehension. In addition, the reverse effect is argued, where it is proposed that language comprehension influences movement and sensation. Proponents of the bi-directional hypothesis of language and action conduct and interpret linguistic , cognitive , and movement studies within the framework of embodied cognition and embodied language processing . Embodied language developed from embodied cognition, and proposes that sensorimotor systems are not only involved in the comprehension of language, but that they are necessary for understanding the semantic meaning of words.

Development of the bi-directional hypothesis

Effects of language comprehension on systems of action, neural activation, effects of systems of action on language comprehension.

Neural activation 2

Organization of neural substrates

Circuit organization, evidence for shared neural networks, external links.

The theory that sensory and motor processes are coupled to cognitive processes stems from action-oriented models of cognition. [2] These theories, such as the embodied and situated cognitive theories, propose that cognitive processes are rooted in areas of the brain involved in movement planning and execution , as well as areas responsible for processing sensory input, termed sensorimotor areas or areas of action and perception. [3] According to action-oriented models, higher cognitive processes evolved from sensorimotor brain regions, thereby necessitating sensorimotor areas for cognition and language comprehension. [2] With this organization, it was then hypothesized that action and cognitive processes exert influence on one another in a bi-directional manner: action and perception influence language comprehension, and language comprehension influences sensorimotor processes.

Although studied in a unidirectional manner for many years, the bi-directional hypothesis was first described and tested in detail by Aravena et al. [1] These authors utilized the Action-Sentence Compatibility Effect (ACE) , a task commonly used to study the relationship between action and language, to test the effects of performing simultaneous language comprehension and motor tasks on neural and behavioral signatures of movement and language comprehension. [1] These authors proposed that these two tasks cooperate bi-directionally when compatible, and interfere bi-directionally when incompatible. [1] For example, when the movement implied by the action language stimuli is compatible with the movement being performed by the subject, it was hypothesized that performance of both tasks would be enhanced. [1] Neural evidence of the bi-directional hypothesis was demonstrated by this study, [1] and the development of this hypothesis is ongoing.

Language comprehension tasks can exert influence over systems of action, both at the neural and behavioral level. This means that language stimuli influence both electrical activity in sensorimotor areas of the brain, as well as actual movement.

Language stimuli influence electrical activity in sensorimotor areas of the brain that are specific to the bodily association of the words presented. This is referred to as semantic somatotopy , which indicates activation of sensorimotor areas that are specific to the bodily association implied by the word. For example, when processing the meaning of the word “kick,” the regions in the motor and somatosensory cortices that represent the legs will become more active. [4] [5] Boulenger et al. [5] demonstrated this effect by presenting subjects with action-related language while measuring neural activity using fMRI . Subjects were presented with action sentences that were either associated with the legs (e.g. “John kicked the object”) or with the arms (e.g. “Jane grasped the object”). The medial region of the motor cortex , known to represent the legs, was more active when subjects were processing leg-related sentences, whereas the lateral region of the motor cortex, known to represent the arms, was more active with arm-related sentences. [5] This body-part-specific increase in activation was exhibited about 3 seconds after presentation of the word, a time window that is thought to indicate semantic processing . [6] In other words, this activation was associated with subjects comprehending the meaning of the word. This effect held true, and was even intensified, when subjects were presented with idiomatic sentences. [5] Abstract language that implied more figurative actions were used, either associated with the legs (e.g. “John kicked the habit”) or the arms (e.g. “Jane grasped the idea”). [5] Increased neural activation of leg motor regions were demonstrated with leg-related idiomatic sentences, whereas arm-related idiomatic sentences were associated with increased activation of arm motor regions. [5] This activation was larger than that demonstrated by more literal sentences (e.g. “John kicked the object”), and was also present in the time window associated with semantic processing. [5]

Action language not only activates body-part-specific areas of the motor cortex, but also influences neural activity associated with movement. This has been demonstrated during an Action-Sentence Compatibility Effect (ACE) task, a common test used to study the relationship between language comprehension and motor behavior. [7] This task requires the subject to perform movements to indicate understanding of a sentence, such as moving to press a button or pressing a button with a specific hand posture, that are either compatible or incompatible with movement implied by the sentence. [7] For example, pressing a button with an open hand to indicate understanding of the sentence "Jane high-fived Jack" would be considered a compatible movement, as the sentence implies an open-handed posture. Motor potentials (MP) are an Event Related Potentials (ERPs) stemming from the motor cortex, and are associated with execution of movement. [8] Enhanced amplitudes of MPs have been associated with precision and quickness of movements. [1] [8] [9] Re-afferent potentials (RAPs) are another form of ERP, and are used as a marker of sensory feedback [10] and attention. [11] Both MP and RAP have been demonstrated to be enhanced during compatible ACE conditions. [1] These results indicate that language can have a facilitory effect on the excitability of neural sensorimotor systems. This has been referred to as semantic priming , [12] indicating that language primes neural sensorimotor systems, altering excitability and movement.

The ability of language to influence neural activity of motor systems also manifests itself behaviorally by altering movement. Semantic priming has been implicated in these behavioral changes, and has been used as evidence for the involvement of the motor system in language comprehension. The Action-Sentence Compatibility Effect (ACE) is indicative of these semantic priming effects. Understanding language that implies action may invoke motor facilitation, or prime the motor system, when the action or posture being performed to indicate language comprehension is compatible with action or posture implied by the language. Compatible ACE tasks have been shown to lead to shorter reaction times. [1] [7] [13] This effect has been demonstrated on various types of movements, including hand posture during button pressing, [1] reaching, [7] and manual rotation. [13]

Language stimuli can also prime the motor system simply by describing objects that are commonly manipulated. In a study performed by Masson et al., subjects were presented with sentences that implied non-physical, abstract action with an object (e.g. "John thought about the calculator" or "Jane remembered the thumbtack"). [14] After presentation of language stimuli, subjects were cued to perform either functional gestures, gestures typically made when using the object described in the sentence (e.g. poking for calculator sentences), or a volumetric gesture, gestures that are more indicative of whole hand posture (e.g. horizontal grasp for calculator sentences). [14] Target gestures were either compatible or incompatible with the described object, and were cued at two different time points, early and late. Response latencies for performing compatible functional gestures significantly decreased at both time points, whereas latencies were significantly lower for compatible volumetric gestures in the late cue condition. [14] These results indicate that descriptions of abstract interactions with objects automatically (early time point) generate motor representations of functional gestures, priming the motor system and increasing response speed. [14] The specificity of enhanced motor responses to the gesture-object interaction also highlights the importance of the motor system in semantic processing, as this enhanced motor response was dependent on the meaning of the word.

A study performed by Dr. Olmstead et al., [15] described in detail elsewhere , demonstrates more concretely the influence that the semantics of action language can have on movement coordination. Briefly, this study investigated the effects of action language on the coordination of rhythmic bimanual hand movements. Subjects were instructed to move two pendulums , one with each hand, either in- phase (pendulums are at the same point in their cycle, phase difference of roughly 0 degrees) or anti-phase (pendulums are at the opposite point in their cycle, phase difference of roughly 180 degrees). [15] Robust behavioral studies have revealed that these two phase states, with phase differences 180 and 0 degrees, are the two stable relative phase states, or the two coordination patterns that produce stable movement. [16] This pendulum swinging task was performed as subjects judged sentences for their plausibility; subjects were asked to indicate whether or not each presented sentence made logical sense. [15] Plausible sentences described actions that could be performed by a human using the arms, hands, and/or fingers ("He is swinging the bat"), or actions that could not be performed ("The barn is housing the goat"). [15] Implausible sentences also used similar action verbs ("He is swinging the hope"). Plausible, performable sentences lead to a significant change in the relative phase shift of the bimanual pendulum task. [15] The coordination of the movement was altered by action language stimuli, as the relative phase shift that produced stable movement was significantly different than in the non-performable sentence and no language stimuli conditions. [15] This development of new stable states has been used to imply a reorganization of the motor system utilized to plan and execute this movement, [15] and supports the bi-directional hypothesis by demonstrating an effect of action language on movement.

The bi-directional hypothesis of action and language proposes that altering the activity of motor systems, either through altered neural activity or actual movement, influences language comprehension. Neural activity in specific areas of the brain can be altered using transcranial magnetic stimulation (TMS), or by studying patients with neuropathologies leading to specific sensory and/or motor deficits. Movement is also used to alter the activity of neural motor systems, increasing overall excitability of motor and pre-motor areas.

Altered neural activity of motor systems has been demonstrated to influence language comprehension. One such study that demonstrates this effect was performed by Dr. Pulvermüller et al. [17] TMS was used to increase the excitability of either the leg region or the arm region of the motor cortex . [17] Authors stimulated the left motor cortex, known to be more closely involved in language processing in right-handed individuals, the right motor cortex, as well as a sham stimulation where stimulation was prevented by a plastic block placed between the coil and the skull. [17] During the stimulation protocols, subjects were shown 50 arm, 50 leg, 50 distractor (no bodily relation), and 100 pseudo- (not real) words. [17] Subjects were asked to indicate recognition of a meaningful word by moving their lips, and response times were measured. [17] It was found that stimulation of the left leg region of the motor cortex significantly reduced response times for recognition of leg words as compared to arm words, whereas the reverse was true for stimulation of the arm region. [17] Stimulation site on the right motor cortex, as well as sham stimulation, did not exhibit these effects. [17] Therefore, somatotopically-specific stimulation of the left motor cortex facilitated word comprehension in a body-part-specific manner, where stimulation of the leg and arm regions lead to enhanced comprehension of leg and arm words, respectively. [17] This study has been used as evidence for the bi-directional hypothesis of language and action, as it showcases that manipulating motor cortex activity alters language comprehension in a semantically-specific manner. [17]

A similar experiment has been performed on the articulatory motor cortex, or the mouth and lip regions of the motor cortex used in the production of words. [18] Two categories of words were used as language stimuli: words that involved the lips for production (e.g. "pool") or the tongue (e.g. "tool). [18] Subjects listened to the words, were shown pairs of pictures, and were asked to indicate which picture matched the word they heard with a button press. [18] TMS was used prior to presentation of the language stimuli to selectively facilitate either the lip or tongue regions of the left motor cortex; these two TMS conditions were compared to a control condition where TMS was not applied. [18] It was found that stimulation of the lip region of the motor cortex lead to a significantly decreased response time for lip words as compared to tongue words. [18] In addition, during recognition of tongue words, reduced reaction times were seen with tongue TMS as compared to lip TMS and no TMS. [18] Although this same effect was not seen with lip words, authors attribute this to the complexity of tongue as opposed to lip movements, and the increase difficulty of tongue words as opposed to lip. [18] Overall, this study demonstrates that the activity in the articulatory motor cortex influences the comprehension of single spoken words, and highlights the importance of the motor cortex in speech comprehension [18]

Lesions of sensory and motor areas have also been studied to elucidate the effects of sensorimotor systems on language comprehension. One such example of this is the patient JR; this patient has a lesion in areas in the auditory association cortex implicated in processing auditory information. [19] This patient showcases significant impairments in conceptual and perceptual processing of sound-related language and objects. [19] For example, processing the meaning of words describing sound-related objects (e.g., "bell') was significantly impaired in JR as compared to non-sound-related objects (e.g., "armchair"). [19] These data suggest that damage of sensory regions involved in processing auditory information specifically impair processing of sound-related conceptual information, [19] highlighting the necessity of sensory systems for language comprehension.

Movement has been shown to influence language comprehension. This has been demonstrated by priming motor areas with movement, increasing the excitability of motor and pre-motor areas associated with the body part being moved. [20] It has been demonstrated that motor engagement of a specific body part decreases neural activity in language processing areas when processing words related to that body part. [20] This decreased neural activity is a feature of semantic priming, and suggests that activation of specific motor areas through movement can facilitate language comprehension in a semantically-dependent manner. [20] An interference effect has also been demonstrated. During incompatible ACE conditions, neural signatures of language comprehension have been shown to be inhibited. [1] Combined, these pieces of evidence have been used to support a semantic role of the motor system.

Movement can also inhibit language comprehension tasks, particularly tasks of verbal working memory. [21] When asked to memorize and verbally recall four-word sequences of either arm or leg action words, performing complex, rhythmic movements after presentation of the word sequences was demonstrated to interfere with memory performance. [21] This performance deficit was body-part specific, where movement of the legs impaired performance of recall of leg words, and movement of the arms impaired recall of arm words. [21] These data indicate that sensorimotor systems exhibit cortically specific "inhibitory casual effects" on memory of action words, [21] as impairment was specific to motor engagement and bodily association of the words.

Relating cognitive functions to brain structures is done in the field of cognitive neuroscience . This field attempts to map cognitive processes, such as language comprehension, onto neural activation of specific brain structures.The bi-directional hypothesis of language and action requires that action and language processes have overlapping brain structures, or shared neural substrates , thereby necessitating motor areas for language comprehension. The neural substrates of embodied cognition are often studied using the cognitive tasks of object recognition , action recognition , working memory tasks , and language comprehension tasks. These networks have been elucidated with behavioral, computational, and imaging studies, but the discovery of their exact organization is ongoing.

It has been proposed that the control of movement is organized hierarchically, where movement is not controlled by individually controlling single neurons, but that movements are represented at a gross, more functional level. [22] A similar concept has been applied to the control of cognition, resulting in the theory of cognitive circuits. [23] This theory proposes that there are functional units of neurons in the brain that are strongly connected, and act coherently as a functional unit during cognitive tasks. [23] These functional units of neurons, or "thought circuits," have been referred to as the "building blocks of cognition". [23] Thought circuits are believed to have been originally formed from basic anatomical connections, that were strengthened with correlated activity through Hebbian learning and plasticity. [23] Formation of these neural networks has been demonstrated with computational models using known anatomical connections and Hebbian learning principles. [24] For example, sensory stimulation through interaction with an object activates a distributed network of neurons in the cortex. Repeated activation of these neurons, through Hebbian plasticity, may strengthen their connections and form a circuit. [23] [25] This sensory circuit may then be activated during the perception of known objects. [23]

This same concept has been applied to action and language, as understanding of the meaning of action words requires an understanding of the action itself. During language and motor skill development, one likely learns to associate an action word with an action or a sensation. [2] [23] This action or sensation, and the correlated sensorimotor areas involved, are then incorporated into the neural representation of that concept. [23] [24] This leads to semantic topography, or the activation of motor areas related to the meaning and bodily association of action language. [4] [5] These networks may be organized into "kernels," areas highly activated by language comprehension tasks, and "halos," brain areas in the periphery of networks that experience slightly increased activation. [23] [24] It has been hypothesized that language comprehension is housed in the left-perisylvian neuronal circuit, forming the "kernel," and sensorimotor regions are peripherally activated during semantic processing of action language, forming the "halo". [23] [24]

Many studies that have demonstrated a role of the motor system in semantic processing of action language have been used as evidence for a shared neural network between action and language comprehension processes. [1] [5] [7] [12] [13] [14] [15] [17] [18] [19] [21] For example, facilitated activity in language comprehension areas, evidence of semantic priming, with movement of a body part that is associated with the action word has also been used as evidence for this shared neural network. [20] A more specific method for identifying whether certain areas of the brain are necessary for a cognitive task is to demonstrate impaired performance of said task following a functional change to the brain area of interest. [26] A functional change may involve a lesion, or altered excitability through stimulation, or utilization of the area for another task. [21] According to this theory, there is only a finite amount of neural real-estate available for each task. If two tasks share a neural network, there will be competition for the associated neural substrates, and the performance of each task will be inhibited when performed simultaneously. [26] Using this theory, proponents of the bi-directional hypothesis have postulated that performance of verbal working memory of action words would be impaired by movement of the concordant body part. [21] This has been demonstrated with the selective impairment of memorization of arm and leg words when coupled with arm and leg movements, respectively. [21] This implies that the neural network for verbal working memory is specifically tied to the motor systems associated with the body part implied with the word. [21] [23] This semantic topography has been suggested to provide evidence that action language shares a neural network with sensorimotor systems, thereby supporting the bi-directional hypothesis of language and action.

Embodied cognition
Embodied language processing
Embodied bilingual language
Situated cognition

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1 2 3 4 5 6 7 8 9 10 11 Pulvermüller, F.; Garagnani, M.; Wennekers, T. (2014). "Thinking in circuits: toward neurobiological explanation in cognitive neuroscience" . Biological Cybernetics . 108 (5): 573–593. doi : 10.1007/s00422-014-0603-9 . PMC 4228116 . PMID 24939580 .
1 2 3 4 Pulvermüller, F., & Garagnani, M. (2014). From sensorimotor learning to memory cells in prefrontal and temporal association cortex: a neurocomputational study of disembodiment . Cortex, 57: 1-21.
↑ Doursat, R., & Bienenstock, E. " Neocortical self-structuration as a basis for learning ." 5th International conference on development and learning (ICDL 2006) . 2006.
1 2 Shallice, T. From neuropsychology to mental structure . Cambridge University Press, 1988.
The real reason for brains , a TED talk by Daniel Wolpert
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What is a Directional Hypothesis? (Definition & Examples)
A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis: Directional hypothesis: The alternative hypothesis contains the less than ("<") or greater than (">") sign. This indicates that we're testing whether or not there is a positive or negative effect. Non-directional hypothesis: The alternative ...
Alternative hypothesis
A two-tailed directional alternative hypothesis is concerned with both regions of rejection of the sampling distribution. Non-directional. A non-directional alternative hypothesis is not concerned with either region of rejection; rather, it is only concerned that null hypothesis is not true. See also.
Directional Hypothesis: Definition and 10 Examples
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 ...
Introduction to Psychology/Research Methods in Psychology
The directional hypothesis states that the change in the independent variable will induce a change in the dependent variable. In a true experiment, all variables are held constant except for the independent variable, which is manipulated. Thus, any changes in the experimental groups can be solely attributed to the action of the independent ...
Bi-directional hypothesis of language and action
The bi-directional hypothesis of language and action proposes that the sensorimotor and language comprehension areas of the brain exert reciprocal influence over one another. [1] This hypothesis argues that areas of the brain involved in movement and sensation, as well as movement itself, influence cognitive processes such as language ...
Directional and non-directional hypothesis: A Comprehensive Guide
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 ...
Hypothesis
The hypothesis of Andreas Cellarius, showing the planetary motions in eccentric and epicyclical orbits.. A hypothesis (pl.: hypotheses) is a proposed explanation for a phenomenon.For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained ...
Research Hypothesis In Psychology: Types, & Examples
Examples. 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.
Understanding Statistical Testing
Directional hypotheses are sometimes introduced as a point hypothesis and a directional hypothesis such as H 0: θ = 0 versus H 1: θ > 0 (Neutens & Rubinson, 2002a; Spanos, 1999a). As indicated by the preceding discussion, this specification is not a general description of directional hypotheses and would only be appropriate when the parameter ...
Directional Test (Directional Hypothesis)
Hypothesis Testing >. A directional test is a hypothesis test where a direction is specified (e.g. above or below a certain threshold). For example you might be interested in whether a hypothesized mean is greater than a certain number (you're testing in the positive direction on the number line), or you might want to know if the mean is less ...
The What, Why and How of Directional Hypotheses
The What: Understanding the Concept of a Directional Hypothesis. A directional hypothesis, often referred to as a one-tailed hypothesis, is an essential part of research that predicts the expected outcomes and their directions. The intriguing aspect here is that it goes beyond merely predicting a difference or connection, it actually suggests ...
Directional Hypothesis
Definition: A directional hypothesis is a specific type of hypothesis statement in which the researcher predicts the direction or effect of the relationship between two variables. Key Features. 1. Predicts direction: Unlike a non-directional hypothesis, which simply states that there is a relationship between two variables, a directional ...
9.1: Hypothesis Tests for Regression Coefficients
Note that this is a directional hypothesis since we are posting a negative relationship. Typically, a directional hypothesis implies a one-tailed test where the critical value is 0.05 on one side of the distribution. A non-directional hypothesis, β≠0β≠0 does not imply a particular direction, it only implies that there is a relationship ...
How to Write a Directional Hypothesis: A Step-by-Step Guide
A directional hypothesis is a statement that predicts the direction of the relationship between two variables. Unlike non-directional hypotheses, which simply state that there is a relationship between variables without specifying the direction, directional hypotheses make a clear prediction about the expected outcome.
What is a Directional Hypothesis? (Definition & Examples)
A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis: Directional hypothesis: The alternative hypothesis contains the less than ("") sign. This indicates that we're testing whether or not there is a positive or negative effect. Non-directional hypothesis: The alternative hypothesis contains the not ...
Directional Hypothesis
A Level Psychology Topic Quiz - Research Methods. Quizzes & Activities. 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).
8.4: The Alternative Hypothesis
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.
Hypotheses; directional and non-directional
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 ...
Beyond Statistical Significance: Clinical Interpretation of
A non‐directional hypothesis, based on rejecting the null hypothesis, provides a reference value for the outcome parameter. A directional hypothesis provides a minimal value for the expected outcome parameter. For example, a directional hypothesis for an intervention that decreases pain by a minimal clinical value may be represented by H 1 > 2.
Bi-directional hypothesis of language and action
The bi-directional hypothesis of language and action proposes that the sensorimotor and language comprehension areas of the brain exert reciprocal influence over one another. [1] This hypothesis argues that areas of the brain involved in movement and sensation, as well as movement itself, influence cognitive processes such as language ...
Olfactory navigation
Olfactory navigation is a hypothesis that proposes the usage of the sense of smell by pigeons, in particular the mail pigeon, in navigation and homing . There are two principal versions. Papi's mosaic model proposes that pigeons construct a map from the distribution of environmental odours, within a radius of 70-100 kilometres.
Central limit theorem for directional statistics
Directional statistics is the subdiscipline of statistics that deals with directions ( unit vectors in Rn ), axes (lines through the origin in Rn) or rotations in Rn. The means and variances of directional quantities are all finite, so that the central limit theorem may be applied to the particular case of directional statistics. [2]
Null hypothesis
The null hypothesis is a default hypothesis that a quantity to be measured is zero (null). Typically, the quantity to be measured is the difference between two situations. For instance, trying to determine if there is a positive proof that an effect has occurred or that samples derive from different batches.

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Directional and non-directional hypothesis: A Comprehensive Guide

Understanding Directional Hypothesis

Definition of directional hypothesis

The role of directional hypothesis in research

Examples of research questions for directional hypothesis

Definition of non-directional hypothesis

Advantages and utility of non-directional hypothesis

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The What, Why and How of Directional Hypotheses

The What: Understanding the Concept of a Directional Hypothesis

The Why: The Significance of a Directional Hypothesis in Research

The How: Constructing a Strong Directional Hypothesis

Pitfalls to Avoid When Formulating a Directional Hypothesis

Putting it All Together: Applying a Directional Hypothesis in Practice

Directional Hypothesis

Key Features

How to Write a Directional Hypothesis: A Step-by-Step Guide

What is a Directional Hypothesis?

Examples of Directional Hypotheses

Why to Write a Directional Hypothesis?

Step 1: Identify the Variables

Step 2: Predict the Direction

Step 3: Use Clear Language

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What is a Directional Hypothesis? (Definition & Examples)

Example 1: Baseball Programs

Example 2: Plant Growth

Example 3: Studying Technique

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Directional Hypothesis

Research Methods: MCQ Revision Test 1 for AQA A Level Psychology

Example Answers for Research Methods: A Level Psychology, Paper 2, June 2018 (AQA)

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