Top 50 Examples of Hypotheses

Hypotheses are a fundamental concept in the scientific method. A hypothesis must be specific enough that it can be proven true or false through testing. This post lists 50 different examples of hypotheses from various fields in science and psychology and how to test the hypothesis. Read on to find out more about them!

Types of Hypotheses

Null Hypothesis (H 0)

A null hypothesis is a hypothesis that hasn’t been tested yet. It means “no effect” or “there’s no difference” when comparing two things. For example, when someone says, “there’s no difference between the amount of energy in a piece of bread and that of an apple,” they’re proposing that there is no effect on the level of satiety.

Null Hypothesis Examples

  • There’s no difference in the number of words written on paper before, and after they’ve eaten three cookies. The person is proposing that there’s no difference in the number of words written.
  • There is no variation between this apple and a piece of bread to satiety.
  • All infants sleep best when placed on their back.
  • Humans will make cutting important decisions using either “gut feelings” or logic, not both types of thinking at the same time.
  • Infants react differently according to specific situations, which can be considered if they possess emotional intelligence from birth.
  • Humans can’t live just on water, fruits, and vegetables
  • People have an innate need to spend money; even when it is more logical to save
  • A television advertisement using the word “because” will be most effective at persuading viewers to purchase a product they’ve seen advertised
  • Negative reinforcement is more effective than positive reinforcement when it comes to punishment
  • Kittens whose eyes were sewn shut will be less able to see than kittens with open eyes
  • Not all sicknesses are linked to viruses or microorganisms
  • The more you weigh, the greater your muscle strength will be
  • The majority of children believe that there is no point in trying to stop bullies
  • The older you are, the less likely you are to make friends with people of a different age group

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Alternate Hypothesis (H 1 / H a)

An alternative hypothesis is a statement that proposes how something works. An alternate hypothesis is a different explanation that could explain the results of an experiment. For example, it’s used in null hypothesis testing to determine whether a new medicine will be effective against the disease. In this case, it would be called a “research hypothesis,” and the research hypothesis can only suggest one.

Alternative Hypothesis Examples

  • A new treatment has an effect more significant than a placebo, while existing treatments only have an impact equal to placebo.
  • An iPhone has a battery life more significant than the Samsung Galaxy S5 since it has a bigger battery.
  • People who regularly drink a lot of wine are 10% more likely to have a heart attack at some point in their lives.
  • A new drug will not reduce the symptoms of depression among people aged 18 to 25 years old.
  • There is no link between how physically fit children are and how well they perform in school.
  • The more often children are read to, the better their reading abilities will be when they grow up.
  • When it comes to complete beginners, men do not have a higher chance of winning an arm wrestle than women.
  • Alcohol does not contribute to making people happier but only encourages them to dance and talk more.
  • There is no link between how often people are on Facebook and their level of happiness.
  • The moment when a woman is most likely to have children has nothing to do with the full moon.
  • Our environment does not affect our life satisfaction. It’s what we make out of it that counts.
  • Both introverts and extroverts can enjoy the same level of happiness in life.
  • People who like alcohol drink it because they want to, not because it helps them relax or causes them to forget their stress
  • If you want to feel more relaxed before an exam, you should spend time with your family than doing extra revision.
  • Short people can run at average speed as fast as their taller counterparts
  • TV advertisements using the word “because” will not be as effective at convincing people to buy products they’ve seen advertised
  • There is no link between how physically fit children are and their self-esteem levels
  • People who exercise regularly are not more likely to experience feelings of depression or anxiety than those who exercise less frequently
  • If you live in a place where there is a little sunshine and rain, you will not be any happier than those who live in areas with frequent showers and sun

Null and Alternative Hypotheses Examples:

Null and Alternative Hypotheses Examples in Physics

The null hypothesis is also denoted as H 0 or H a. Alternatives are denoted as H 1.

  • The temperature on Mars Null hypothesis: The temperature on Mars is not different from the average temperature on Earth.
  • Alternative hypothesis: The temperature on Mars is different from the average temperature on Earth.
  • Quantum Mechanics Null hypothesis: Scientists don’t have complete knowledge about how particles behave.
  • Alternative hypothesis: Scientists have complete knowledge about how particles behave.

Null and Alternative Hypotheses Examples in Chemistry

  • The acidity of a substance null hypothesis: The acidity of a particular substance is not different from another.
  • Alternative hypothesis: The acidity of a particular substance is different from another.

Null and Alternative Hypotheses Examples Related to Photography

  • Null:  Smaller sensors have the same performance as larger sensors in every way.
  • Alternative:  There are some things that smaller sensors are better at and some things that larger sensors are better at.
  • Null:  Different lenses do not affect how sharp the resulting image is.
  • Alternative: Different lenses do affect how sharp the resulting images are.
  • Null:  A camera with an APS-C or Micro Four Thirds sensor has a lower dynamic range than a camera with a full-frame sensor.
  • Alternative: A camera with an APS-C or Micro Four Thirds sensor has a similar dynamic range to a camera with a full-frame sensor.
  • Null:  Noise does not affect the overall image quality of a photo.
  • Alternative: Noise does affect the overall image quality of a photo.

Null and Alternative Hypotheses Examples in Plant Biology

  • Null:  The addition of fertilizer to the soil does not increase yield.
  • Alternative: The addition of fertilizer to soil increases yield.
  • Null:  There is no difference between the amount of biomass produced when these two plants are grown together rather than separately.
  • Alternative: There is a difference between the amount of biomass produced when these two plants are grown together than separately.

Null and Alternative Hypotheses Examples in Genetics

  • Null:  The genetic material in an organism doesn’t affect how it looks in the end.
  • Alternative: The genetic material in an organism does affect how it looks in the end.
  • Null:  The performance of this apple is not different from that of an orange.
  • Alternative: The performance of this apple is different from that of an orange.
  • Null and Alternative Hypotheses Examples in Sociology
  • Null:  People with higher IQ scores are no more or less happy than people with lower scores.
  • Alternative: People with higher IQ scores are more or less happy than people with lower scores.
  • Null:  There is no difference between the happiness of men and women.
  • Alternative: There is a difference between the happiness of men and women.

Null and Alternative Hypotheses Examples in Linguistics/Literature

  • Null:  There is no difference between the number of errors a child makes when writing and how well they spell.
  • Alternative: There is a difference between the number of errors a child makes when writing and how well they spell.
  • Null:  There is no effect on students’ grades from technology rather than without it.
  • Alternative: There is an effect on student’s grades from being taught with technology rather than without it.

Null and Alternative Hypotheses Examples in Behavioral Economics

  • Null: Changes to the prices of goods do not affect how many people buy them.
  • Alternative: Changes to the prices of goods affect how many people buy them.
  • Null: Changes to the advertising campaigns for these goods do not affect how many people buy them.
  • Alternative: Changes to the advertising campaigns affect how many people buy these goods.

Null and Alternative Hypotheses Examples in Bioinformatics and Computational Biology (Molecular Biology)

  • Null:  There is no variation between the effects of this drug and that drug on a tumor’s size.
  • Alternative: There is a variation between the effects of this drug and that drug on a tumor’s size.
  • Null:  The expression levels of these two genes are not related to each other.
  • Alternative: The expression levels of these two genes are related to each other.   
  • Null:  Changes in chromosome number do not directly cause changes in chromosome morphology.
  • Alternative: Changes to chromosome number do directly cause changes to chromosome morphology.     
  • Null and Alternative Hypotheses Examples in Mathematics/Computer Science/Statistics
  • Null:  The performance of these two algorithms are not different from one another.
  • Alternative: The performance of these two algorithms is different from one another.
  • Null:  The weight of these two objects is not related to how many times they will sink in the water.
  • Alternative: The weight of these two objects is related to how many times they will sink in the water.

Null and Alternative Hypotheses Examples in Forensic Science/Physical Anthropology/Criminology

  • Null:  There is no difference between the fairness of these two trials.
  • Alternative: There is a difference between the fairness of these two trials.
  • Null:  There are no racial or socioeconomic differences associated with this crime pattern.
  • Alternative: Racial or socioeconomic differences are associated with this crime pattern.     
  • Null and Alternative Hypotheses Examples in Biology
  • Null: The expression of gene A is independent of the expression of gene B.
  • Alternate hypothesis: The expression of gene A is dependent on the expression of gene B.
  • Null: There is no difference between samples 1 and 2.
  • Alternative hypothesis: There is a difference between samples 1 and 2.

Null and Alternative Hypothesis Examples in Chemistry

  • Null: The rate of reaction is independent of the concentration of A.
  • Alternative hypothesis: The rate of reaction is dependent on the concentration of A.
  • Null: The average kinetic energy for a gaseous molecule is independent of its mass.
  • Alternative hypothesis: The average kinetic energy for a gaseous molecule is dependent on its mass.

Null and Alternative Hypotheses Examples in History

  • Null hypothesis: The US Civil War (1861-65) was not fought against emancipation states that practiced slavery.
  • Alternative hypothesis: The US Civil War (1861-65) was fought against emancipation states that practiced slavery.
  • Null hypothesis: There is no difference in political orientation between employees of a particular organization and the members of its board of directors.
  • Alternative hypothesis: There is a difference in political orientation between employees of a particular organization and the members of its board of directors.

Null and Alternative Hypotheses Examples in Medicine

  • Null hypothesis: Patients who receive treatment A have an identical outcome as those who receive treatment B.
  • Alternative hypothesis: Patients who receive treatment A have a different outcome than those who receive treatment B.
  • Medical scientists infer the accuracy of their alternate hypotheses by evaluating the statistical significance and other parameters like P values. If they reject the null hypothesis, there is enough evidence to support an alternative hypothesis.

Null and Alternative Hypotheses Examples in Physics

  • Null hypothesis: A student’s final grade is independent of the number of hours he spent studying.
  • Alternative hypothesis: A student’s final grade depends on the number of hours he spent studying.
  • For example, a physicist might hypothesize that a specific relationship exists between two variables. If she experiments to determine the relationship and her data does not support the hypothesis, she will reject the null hypothesis in favor of its alternative.
  • Null hypothesis: The Sun will rise tomorrow.
  • Alternative hypothesis: The Sun will not rise tomorrow.
  • Null hypothesis: There is no difference in political orientation between employees of a particular organization and the members of its board of directors.
  • Alternative hypothesis: There is a difference in political orientation between employees of a particular organization and the members of its board of directors.

Null and Alternative Hypotheses Examples in Psychology

  • Null hypothesis: A survey found no difference between people playing video games and those not concerned about their stress levels, evaluated through a stress test.
  • Alternative hypothesis: There is a difference between people who play video games and those who don’t concerning their stress levels, evaluated through a stress test.

In this study, the null hypothesis would be that there is no difference between two samples of people regarding their stress levels. Suppose the data indicated a significant difference between the groups. In that case, the researcher could reject the H 0 (null hypothesis) and conclude that playing video games is associated with a reduced stress level.

  • Null hypothesis: There is no difference between the two samples.
  • Alternative hypothesis: There is a difference between the two samples.

In a survey, a researcher might ask participants how often they use social media sites like Twitter or Facebook to determine if there’s any association between their usage of these sites on the one hand and their happiness on the other.

The null hypothesis would state no correlation between the frequency of social media use and happiness. In contrast, the alternative hypothesis would say that a greater frequency of social media use is associated with increased happiness.

If the data supports the alternative hypothesis, the researcher must have made a mistake in the original assumption. In this case, she should reject H 0 (null hypothesis) and conclude that her data supports the alternative hypothesis.

If the data does not support either of these hypotheses, then it is “inconclusive” and must be tested again to see if any correlation exists.

These hypotheses are the opposite of experimental predictions. They assume no relationship; that variables are independent or unrelated.

Operational Hypothesis

An operational hypothesis takes an input, runs it through the process that has been proposed to cause something, and then outputs the result. For example, “When I drop this ball, it will fall towards the ground at 9.8 m/s.”

Examples of Operational Hypothesis

  • If I eat food, then my hunger decreases.
  • If I press the gas pedal in my car, then the speed of my car increases.
  • If I am angry, then my heart rate increases.
  • If I get a flu vaccination, then antibodies against influenza develop in my body and will protect me from future infections by this type of virus. (The null hypothesis is that there is no difference in the number of cases of influenza that occur over the next year between people who are vaccinated against influenza and those who are not).

The core principle is that if you can change something (input) and measure its effect on something else (output), there is cause-and-effect. If changing one thing, called the independent variable (cause), can lead to changes in another thing, called the dependent variable (effect), then it is scientific.

If you change something and you don’t get the expected result, or if there was no effect at all on something else, then either you didn’t properly run the experiment (so go back and try again) or there is no cause-and-effect relationship.

This form of hypothesis testing allows the researcher to determine whether the difference observed is statistically significant or not.

Directional Hypothesis

A directional hypothesis predicts the direction of an effect but not the magnitude. For example, if someone says, “My math test scores will be higher than my English test scores,” they’re predicting that their math scores will be higher than their English scores, but they aren’t making any claims about how much higher or how confident they are about their prediction.

The directional hypothesis can also be framed as “more” or “will be higher/greater/etc.” For example, if someone says, “My math test scores will be more than my English test scores,” they’re predicting that their math scores will be greater than their English scores.

Direct Directional Hypothesis Examples

  • My math test scores will be higher than my English test scores.
  • My child will be more talkative with me after watching a cartoon.
  • When I take this drug, my headache will decrease in severity, with the null hypothesis being that taking medicine does not affect headache severity.

Non Directional Hypothesis

A non-directional hypothesis states a relationship exists between two measured phenomena, but it is unclear which direction the relationship lives in.

An example of this would be “There exists a relationship between the time spent sleeping and academic performance.” The null hypothesis could be either that there is no relationship or that there exists a positive relationship.

In other words, a non-directional hypothesis predicts the direction of an effect, but not the magnitude or even whether it will happen at all.

If someone says, “My math test scores will go up when I study more,” then they’re predicting that their math scores will increase after studying but don’t have an expected magnitude.

Non-Directional Hypothesis Examples

  • There exists a relationship between the time spent sleeping and academic performance.
  • This drug will decrease my headache severity, with the null being no effect on headache severity.
  • When I take this drug, my nausea will decrease, with the null hypothesis being no effect on nausea severity.

Converse Hypothesis

The converse of a directional hypothesis is formed by flipping the direction of the prediction. For example, if someone says, “My math test scores will be higher than my English test scores,” then their converse would be, “My English test scores will be higher than my math test scores.”

Two-Tailed Hypothesis

A two-tailed hypothesis states that data points will be evenly distributed on each side of an expected mean. It is measured by a two tailed test. A two tailed test is a test in which the critical region is divided evenly between two tails. An example of this would be, “I believe that people are more likely to have blue than green eyes.” The null hypothesis in this experiment is that the average number of blue eyed people equals the average number of green eyed people.

Examples of Two-Tailed Hypotheses

  • The average height of women in this town is 5’6. There are no restrictions on the distribution of data points.
  • The average time for an animal to evolve into a new species is 5 million years.
  • The average bird flies at 20 meters per second.
  • The average child can outrun its parents in 100 meters.
  • There is no correlation between headache severity and taking this drug.
  • There is no relationship between how fast you run and your heart rate after running 5 miles.

One-Tailed Hypothesis

A one-tailed hypothesis states that there will be an uneven distribution of data points, with most on one side of an expected mean. It is measured by one tailed test.

One tailed test is any statistical hypothesis test in which the critical region of the distribution falls entirely on one side of the mean (in either tail). In this case, a one-tailed hypothesis example would be, “There is a correlation between physical fitness and academic performance.”

The expected mean would reflect the directionality of this relationship – i.e., in this case, it would be that higher levels of physical fitness are associated with better academic performance. The null hypothesis could be either that there is no relationship or that there exists a negative relationship.

In other words, the one-tailed hypothesis states that the data will be centered around an expected mean, with most of the data on one side (either the left or right) of this mean.

One-Tailed Hypothesis Examples

  • I believe that my dog has developed psychic abilities. Hypothesis testing for this would be a one-tailed test because the hypothesis states that psychic abilities do exist.
  • I believe that my dog can read minds. Hypothesis testing for this would be a one-tailed test because the hypothesis states that my dog can read minds.
  • The average bird flies at 20 meters per second.
  • The average child can outrun its parents in 100 meters.
  • The average growth of corn is 10 inches per day.

Tails represent possibilities of the data set falling in either direction of an expected mean; while the one-tailed test is more specific, it does not have to be true.

When Should You Use Which Hypothesis?

1) If you predict that nothing will happen, then you’re using the null hypothesis (H0). Your experiment has to prove this wrong to make any progress toward the alternate hypothesis (H1)–that something did happen.

2) If you predict that A WILL happen, you’re using the Alternate hypothesis (H 1). Your experiment has to prove this wrong to make any progress toward the null hypothesis (H 0)–that nothing did happen.

3) If you predict that A OR B will happen, then you’re using the two-tailed hypothesis (H1/2).

How Students may Use a Hypothesis in an Experiment

1) Identify what you think is going to happen. For example, if you want to test whether adding too many items makes it harder for people to recall them later, write down your prediction.

2) Describe why you think this is going to happen. If it happens because of a particular reason, write this down as well.

3) Write down an experiment that can prove your prediction wrong and show that it’s not true. For example, suppose the item overload hypothesis explains that adding items makes recall harder by taking up storage space. In that case, your experimental method should have it so that the added items are never retrieved. This is one way to show that adding too many things doesn’t hurt recall.

4) Experiment! If you predicted item overload would happen, you now need to design an experiment with this definition in mind. To do so, ask yourself, “What variables must be controlled, and how do I keep them constant? What features of my experiment can vary?”

5) Analyze the data you collect. If your prediction is true (i.e., that adding items DOES make recall harder), then some variables will be significant and others won’t. For example, suppose the number of added items significantly affects memory, but the order in which they’re added doesn’t, then you have strong evidence that the hypothesis is true. If your prediction is false (i.e., adding items DOESN’T make recall harder), then some variables will be significant, and others won’t. In this case, too, if the number of added items isn’t a significant predictor of recall, then your hypothesis is false.

6) Write a conclusion about your results and appropriately report them. Your conclusion should acknowledge that you experimented, not just jumped to a conclusion without data or evidence! If you have a strong prediction that deviates from the null, then go ahead and make this clear in your findings.

7) Suppose your experiment tests two null hypotheses (that A and B both occur or that A and B don’t occur), then your experimental results should show evidence for one but not the other.

For example, suppose you think people will be less able to recall items they saw in a particular order than ones they saw out of order. In that case, your experimental results should show that recall is significantly affected by order when the number of items being recalled is low, but not when it’s high.

If you have a two-tailed hypothesis about order and recall, your experiment might test whether either order or number affects memory–in which case the results could be significant for both variables.

How to Write a Null Hypothesis

Step 1: State the null hypothesis

Null Hypothesis-The amount of time people take to complete a cognitive task will not affect how well they perform that task.

Step 2: State the alternative hypothesis

Alternative Hypothesis- The amount of time people take to complete a cognitive task will affect how well they perform that ta

Step 3: State what you are testing

We are testing whether or not the time taken to complete the task affects the accuracy of recall.

Step 4: Define your variables.

There will be two independent variables- Time (T) and Order (O). There will also be one dependent variable- Accuracy (A)

  • The null hypothesis is always going to be the opposite of your alternative hypothesis.

In null hypothesis writing, you first ask a question, then rephrase the question in the form of a null hypothesis.

For instance;

QuestionNull HypothesisAlternative hypothesis
Are teens better at math than adults?Age does not affect mathematical abilityAge affects mathematical ability.
Will a dog’s barking increase after listening to loud music?Loud music does not affect a dog’s barking.Loud music affects a dog’s barking.

To write a null hypothesis, you need to use the following symbols:

  • H 0: Null hypothesis
  • H 1 or H a: Alternate hypothesis

There is no significant difference between the means of the samples or populations, which is symbolized as H 0 μ 1 = μ 2 or H 0 μ 1 – µ 2 = 0.

Null hypotheses in psychological research can also be in the form of a directional or non-directional hypothesis.

A null hypothesis in which no significant difference between groups can either have one group being significantly higher than the other. Its symbolized as H 0 μ 1 – μ 2 > 0 or it can simply not have a significant difference between the two, designated as H 0 μ 1 = μ 2.

Once you have written your null hypothesis, it will act as the “what we expect” (or the old way of thinking), while your alternative hypothesis is more like “what some people believe” (the new or revised way of thinking).

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Hypothesis Testing- Methods

Scientists conducting experiments for hypothesis testing may use null hypotheses to find out whether their results are valid or evidence against the hypothesis they want to prove.

1. Statistical Hypothesis Testing

Null hypotheses are tested using statistical tests. They quantify the probability that an observed difference between parameters (for example, a regression coefficient in a linear model) may have occurred by chance if the null hypothesis is true.

Thus, such a test amounts to checking whether or not the observed data are consistent with the null hypothesis. Reject H 0 if any of these probabilities are less than or equal to a significance level- often chosen as 0.05, 0.01, or 0.001 (the p-value).

2. Experimental Inquiry

An experimental inquiry is a plan for testing an experiment with two or more independent variables to determine if there are any cause and effect relations.

Scientists conduct experiments to test the validity of their hypotheses by using alternative hypotheses, operational definition, prediction, control group, operational procedure, operational manipulation, independent variable, dependent variable.

Reject H 0 if the experiment results show that there is a cause and effect relationship between the independent and dependent variables.

3. Analyzing Variance (ANOVA)

A hypothesis test in which the null hypothesis states that there is no difference between experimental groups, whereas the alternative hypothesis states that there are differences between experimental groups.

For results, reject H 0 if F > F*. F is the calculated value from the ANOVA table, F* critical value from a chi-square distribution with “n” – 1 degree of freedom at p = alpha/2 (e.g., if 5% significance level then α = 0.05) n number of experimental groups. (E.g., a 2-group test would be a one-tailed test because there is only one alternative hypothesis that the difference between groups is significant).

4. Correlation and Regression Analysis

A statistical relationship of variables such as X and Y where one variable may affect the other such as high blood pressure (X) affecting hypertension (Y).

A correlation coefficient measures the strength and direction of a linear relationship between two variables. Linear regression is a statistical method for estimating the relationships among variables.

Sample problem: Blood pressure (X) affects hypertension (Y). Predict high systolic pressure from diastolic blood pressure, age, and weight using linear regression analysis with SPSS software.

a. Linear regression predicting Y from X & Z

Input data: High systolic pressure (X), Diastolic blood pressure (Y), age, and weight.

  • The operational definition of high systolic pressure should include units as mmHg or “mm Hg.”
  • The operational definition of diastolic blood pressure should include units as mmHg or “mm Hg.”
  • The operational definition of age should include units as years.
  • The operational definition of weight should include units as kilograms or kg.

The hypothesized prediction equation is:

Y = a + bX + cZ. Y is the predicted value for a given X, Z from the regression analysis.

“a” through “c” are the regression coefficients, “b” is the slope of Y with respect to X.

R2 = percentage of variability in Y that can be explained by changes in X.

RSG = percentage of variability in Y that can be explained by changes in X and Z.

F statistic value may be used as a test statistic for testing the null hypothesis that b1 = 0 versus the alternative hypothesis that b1 ≠ 0.

The critical F statistic value for 5% significance level is 3.27 (p < .05).

b. Linear regression predicting Y from X, Z & W

Input Data: High systolic pressure (X), Diastolic blood pressure (Y), Age, Weight, and Gender.

  • The operational definition of high systolic pressure should include units as mmHg or “mm Hg.”
  • The operational definition of diastolic blood pressure should include units as mmHg or “mm Hg.”
  • The operational definition of age should include units as years.
  • The operational definition of weight should include units as kilograms or kg.

The hypothesized prediction equation is:

Y = a + bX + cZ + dW. Y is the predicted value for a given X, Z, W from the regression analysis.

“a” through “d” are the regression coefficients, “b” is the slope of Y with respect to X.

R2 = percentage of variability in Y that can be explained by changes in X.

RSG = percentage of variability in Y that can be explained by changes in X, Z, and W.

Null hypotheses are used in statistical testing for two primary purposes:

  • To test the strength of evidence against the null hypothesis. (More specifically, to determine if we can reject it), e.g., in a fair coin trial, the null hypothesis is that the coin is unbiased towards heads or tails. The alternative hypothesis is that the coin is biased towards one side or the other.
  • To estimate a quantity of a population. E.g., in a sample study of 750 people who have cancer, it might be essential to evaluate how many have cancer. One sample statistic that is used to estimate the proportion of people in a population who have cancer, given that we already know the ratio in the sample is 0.1, which means 10% of people have cancer

Null hypothesis testing rejects H 0 when they are false. That means there is evidence to support H 1. You should not claim it as proven false or true.

If we can prove that there is a relationship between two variables, we can use this to help us make predictions and strengthen relationships in the future.

The alternate hypothesis is usually denoted as H a or H 1 and is the opposite of the null hypothesis, H 0.

An experiment can help to prove if the null hypothesis is true or false by using the alternate hypothesis as an outcome to compare with. For example, if it’s found that there are more words written on paper after eating three cookies than before eating them, then the null hypothesis is false.

5. Null Hypothesis Test (NHST)

In null Hypothesis testing, you may accept or reject the null hypothesis by analyzing the results of an experiment. This method was greatly criticized due to its widespread misuse in psychological and academic research. However, it is still widely used.

NHST is based on the probability that we would get equal to or more extreme results than what we observed if the null hypothesis was true.

If your experiment has a couple of different potential outcomes, you could reject or fail to reject the null hypothesis. If you reject the null hypothesis, this means that there is evidence to suggest that your alternate hypothesis is correct. Thus, your alternative hypothesis should be accepted in lieu of the original one.

Null Hypotheses take six major forms:

1. The correlation coefficient between X and Y is equal to zero (H 0 : r XY = 0)

2. The population mean of Y given a value of X is equal to the population mean of Y given a value of X (H 0 μ YX = µ YX)

3. The regression equation relating X and Y has an R2-value that is equal to zero (H 0 : R2XY = 0)

4. The means of two populations, matched on values of a third variable, are equal (H 0 μ 1 = µ2)

5. The distribution of scores on two variables is the same (H 0 : v1 = v2)

6. The expected value of the product of paired measurements equals the product of their mean values (H 0: E (XY) =E(X) E(Y))”.

  • Null hypotheses are generally denoted by H 0 and pronounced “H-naught” or “H-zero.”
  • The null hypothesis, when shown false, leads to a conclusion that the hypothesis was not supported.
  • The null hypothesis is generally preferred over an alternative hypothesis

6. Two-Sided Test

This test assumes that any variation between our experimental data and the expected outcome could be chance. In other words, there are no restrictions on what values can be produced from one set of data or the other.

7. One-Sided Test:

This type of hypothesis testing examines the direction of the effect rather than its size or statistical significance. For example, you can say that “there’s an effect” instead of “there’s no difference.” This type of hypothesis test is common in various fields, such as psychology and economics.

Other tests include scientific investigation, absolute proof, risk-benefit analysis, and problem-solving approach.

Conclusion

Hypotheses are essential in scientific research. They make conclusions about how events occur, how certain factors affect things, and what can happen if one thing changes. They’re used to help us understand the world around us. They make connections that help to explain why things happen.

A hypothesis is a testable prediction about the relationship between variables that can be supported or refuted through a scientific investigation.

It requires a question that can be answered by science. Such as experiments or evidence-based research. If a hypothesis is proven wrong, it can often be reformulated into another testable hypothesis.

A hypothesis is only accepted as true once tested multiple times and proven to be evidence-based.

The null hypothesis is denoted as H 0.  H 0 states no relationship between variables or groups (e.g., group A is equal to group B). The alternate hypothesis is denoted as H a or H 1.  It states a relationship between variables or groups (e.g., group A is not equal to group B).

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