50 Examples of Null Hypotheses
A null hypothesis is a theory that is currently unproven but may still be true.
The term “null” refers to the fact that there is no expected difference between two groups or phenomena being examined under this type of hypothesis. For instance, if you are testing whether men and women had different heights on average, a null hypothesis would be: there was no expected difference between these two variables (i.e., their height) when looking at them together – hence the name “null.”
Every null hypothesis (H 0) usually has a corresponding alternative hypothesis tested to prove the former true or false.
The process of writing a null hypothesis happens in these two steps:
- Stating a question
- Re-stating the question to show a non-correlation between variables
Top 50 Null Hypotheses Examples
Example 1
Question: Does someone’s height affect their ability to play basketball?
Null hypothesis: There is no correlation between height and the ability to play basketball.
Example 2
Question: Is there a difference between the resting metabolic rates of men and women?
Null Hypothesis: The resting metabolic of individuals is not dependent on gender
Question: Does the number of accidents in a month depend on the length of time drivers have held their licenses?
Null Hypothesis: There is no correlation between the number of accidents in a month and how long drivers have held their licenses.
Example 4
Question: Is there a relationship between people’s political leanings (conservative, moderate, liberal) and their views on trade unions?
Null Hypothesis: There is no correlation between people’s political leanings and their views on trade unions.
Example 5
Question: Does the number of accidents in a month depend on drivers’ hours and distance traveled?
Null Hypothesis: The number of accidents in a month does not depend on drivers’ hours, and distance traveled.
Example 6
Question: Does personality depend on the timing of birth?
Null Hypothesis: Personality does not vary depending on the time of day a person is born.
Example 7
Question: Is there a difference between the number of boys and girls entering primary education?
Null Hypothesis: There is no difference between the number of boys and girls entering primary education.
Example 8
Question: Is there a relationship between age and lung function (measured by forced expiratory volume) in adults?
Null Hypothesis: There is no correlation between age and lung function in adults.
Example 9
Question: Are men better than women at solving problems?
Null Hypothesis: There is no difference between men and women when it comes to problem-solving.
Example 10
Question: Does a person’s eye color affect their driving ability?
Null Hypothesis: Eye color does not affect a person’s driving ability.
Example 11
Question: Is there a correlation between the number of newborn babies with down syndrome and parental age?
Null Hypothesis: The number of newborn babies with down syndrome is not correlated to parental age.
Example 12
Question: Are people’s academic achievements (e.g., grades) independent of age?
Null Hypothesis: A person’s academic achievement is not dependent on their age.
Example 13
Question: Is there a relationship between age and the volume of the hippocampus?
Null Hypothesis: There is no correlation between age and hippocampus volume
Example 14
Question: Does the number of boys and girls entering primary education depend on when in the school year they are born?
Null Hypothesis: The number of boys and girls entering primary education does not depend on when in the school year they are born.
Example 15
Question: Are there any differences between the memories of younger and older citizens?
Null Hypothesis: There is no difference in memory capabilities based on age.
Example 16
Question: Does the location of a person’s brain damage affect their ability to solve insight problems?
Null Hypothesis: A person’s ability to solve insight problems is not affected by the location of brain damage.
Example 17
Question: Is there a difference in height between men and women when controlling for age?
Null Hypothesis: There is no difference in height between men and women when controlling for age.
Example 18
Question: Is there a link between right-handedness and the ability to adapt to unusual circumstances?
Null Hypothesis: There is no relationship between right-handedness and adaptability to unusual circumstances.
Example 19
Question: Are there differences in the symptoms of depression between men and women?
Null Hypothesis: Depression symptoms are not dependent on gender
Example 20
Question: Does the average age of first-time fathers depend on the year of birth?
Null Hypothesis: The average age of first-time fathers is unrelated to the year of birth.
Example 21
Question: Are boys more book smart than girls in the same age group?
Null Hypothesis: There is no correlation between gender and being book smart
Example 22
Question: Is there a relationship between birth month and temperament?
Null Hypothesis: There is no relationship between birth month and temperament.
Example 23
Question: Does the number of men and women in prison depend on when they were born?
Null Hypothesis: There is no relationship between the number of men and women in prison and their time of birth.
Example 24
Question: Are there differences in intelligence between right-handed and left-handed people?
Null Hypothesis: There is no difference in intelligence between right-handed and left-handed people.
Example 25
Question: Are there differences in the spatial abilities of men and women?
Null Hypothesis: There is no difference between male and female performance on a three-dimensional spatial task.
Example 26
Question: Is there a difference in the symptoms of schizophrenia between men and women?
Null Hypothesis: Schizophrenia symptoms do not depend on the gender of the patient
Example 27
Question: Is there a relationship between the shape of a face and personality traits?
Null Hypothesis: Personality traits do not depend on the shape of a person’s face.
Example 28
Question: Is there a relationship between birth order and personality?
Null Hypothesis: There is no relationship between birth order and personality.
Example 29
Question: Is there a link between a person’s natural hair color and personality?
Null Hypothesis: There is no correlation between a person’s natural hair color and personality.
Example 30
Question: Is there an effect of birth order on the number of children in families?
Null Hypothesis: There is no relationship between birth order and the number of children in a family.
Example 31
Question: Is there a positive relationship between a person’s age and the time taken to complete tasks?
Null Hypothesis: A person’s age is not related to the time taken to complete tasks.
Example 32
Question: Are there differences in people’s political views based on what they read?
Null Hypothesis: There is no relationship between what a person reads and their political views.
Example 33
Question: Does birth order affect the age at which people get married?
Null Hypothesis: There is no relationship between birth order and age of marriage.
Example 34
Question: Is there a difference in the symptoms of amyotrophic lateral sclerosis between men and women?
Null Hypothesis: Amyotrophic lateral sclerosis symptoms are not different in men and women.
Example 35
Question: Is there a relationship between the chances of winning the lottery and birth date?
Null Hypothesis: The chances of winning the lottery do not depend on a person’s birth date.
Example 36
Question: Is there a difference in the symptoms of anxiety between men and women?
Null Hypothesis: Anxiety symptoms in men do not differ from those in women
Example 37
Question: Is there a relationship between natural hair color and skin color?
Null Hypothesis: Skin color is not related to natural hair color.
Example 38
Question: Are there differences in the risk of developing mental disorders based on the person’s birth month?
Null Hypothesis: There is no relationship between birth month and risk of developing a mental disorder.
Example 39
Question: Is there a relationship between the number of sexual partners and stress level?
Null Hypothesis: Stress level is not related to the number of sexual partners.
Example 40
Question: Is there a difference in pain tolerance between men and women?
Null Hypothesis: Pain tolerance is not dependent on gender
Example 41
Question: Is there a relationship between the shape of the ear and personality traits?
Null Hypothesis: Personality traits do not depend on the shape of a person’s ear.
Example 42
Question: Is there a difference in voter turnout between men and women?
Null Hypothesis: There is no correlation between voter turnout and gender
Example 43
Question: Are there differences in the types of music people enjoy based on their political views?
Null Hypothesis: There is no relationship between a person’s political views and the types of music they enjoy.
Example 44
Question: Is there a relationship between working memory capacity and age?
Null Hypothesis: Working memory capacity is not related to a person’s age.
Example 45
Question: Does a person’s age affect the likelihood of having a child with autism?
Null Hypothesis: There is no relationship between a person’s age and the likelihood of having a child with autism.
Example 46
Question: Is there a relationship between the number of hours spent watching TV and school grades?
Null Hypothesis: School grades are not related to the number of hours spent watching TV.
Example 47
Question: Are there differences in the amount of computer time people use based on their sex?
Null Hypothesis: There is no relationship between a person’s sex and the amount of computer time they use.
Example 48
Question: Does the type of school attended affect the average number of hours spent sleeping per day?
Null Hypothesis: The type of school that a person attends does not affect the number of hours per day that they sleep.
Example 49
Question: Does the number of hours spent sleeping affect school grades?
Null Hypothesis: There is no relationship between the number of hours spent sleeping and school grades.
Example 50
Question: Is there a relationship between blood type and personality?
Null Hypothesis: Blood type and personality are not related.
Null Hypothesis Testing
It is a statistical method for testing the validity of an assumption. In research, null shows a non-existent relationship between the observed variables.
There are two types of variables: the dependent variable and the independent variable.
The independent variable is manipulated by the researcher, while the dependent variable is usually observed as a result.
Null hypotheses assume that there is no statistical relationship between the independent and dependent variables. This means that any changes in the independent variable will not affect the dependent variable.
The hypotheses are then evaluated by comparing the results with the expectations set by the null hypothesis. If there is a difference between them, then that is evidence that the null hypothesis may be incorrect. The alternative hypothesis states that there is a relationship between the two variables.
Types of Statistical Tests
There are several types of statistical analysis tests. These include:
One sample t-test
It is used to estimate the mean for a single group. The null hypothesis is that the mean of a single population equals a tested value.
Z-test
This hypothesis test is used to assess the statistical significance between the mean of a sample and a given reference value.
Two sample t-test
It compares the means for two different groups. The null hypothesis is that the means for both groups are equal.
Correlation
Correlation finds the relationship between variables that may not be related in ways that are not easily determined.
Chi-Square (χ²) Goodness of fit test
It is used to assess whether a theoretical distribution of data matches the actual observed frequency distribution.
Chi-Square (χ²) Test for independence
This Chi-square test is used to determine if two categorical variables are related or not.
Multiple Regression
It is used to predict the value of a continuous dependent variable, given the values of several independent variables. This test is used to assess a statistically significant linear relationship between the independent and dependent variables.
ANOVA
The ANOVA test finds out if there is a significant difference between the means of several groups.
Fisher’s exact test
It is used to determine if the probability of an event is different from what is expected under the null hypothesis.
Statistical significance
Null hypotheses can also be used to determine if something is statistically significant.
Statistical significance indicates the strength of evidence that an observed effect is not attributable to chance.
If an event comes about through chance, then it doesn’t have a cause. If the observed evidence strongly suggests a relationship between two factors exists, it shows a lack of statistical significance.
The strength of evidence that an event is statistically significant can be measured by its probability value. This is also known as P-value
A low probability value indicates that the event is unlikely to result from chance—the lower the probability value, the stronger the evidence.
A low P-value doesn’t necessarily mean that the null hypothesis is false. It just means that the observed evidence suggests that it might be true. However, there’s enough doubt or insufficiencies in the data that it can not be accepted.
Examples of significance tests are one-tail and two-tail tests.
A one-tailed test
A statistical significance test is used when the research hypothesis predicts that a certain value has a greater effect than other values. In this case, the null hypothesis assumes that no value is better than any other.
A two-tailed test
This statistical significance test is applied where the research hypothesis predicts that there will be some difference between groups or values. The null hypothesis assumes that there will be no difference at all.
A crucial aspect of any statistical test is determining whether the null hypothesis should be rejected. After performing a statistical test, a probability value often reflects how likely it is that the difference between the observed and expected values is due to chance.
If this probability value is very low, then it means that there is a small likelihood that the difference was due to chance. This is evidence that the null hypothesis may not be true and can be rejected.
Other Statistical Terms
- Critical value-It is the score a sample needs to reject a null hypothesis in a test statistic.
- Effect size-It is a measure of how much of an effect exists between groups.
- Explanatory variable-It is a variable whose values are used to predict another variable.
- Inference-It can be defined as the process of drawing conclusions from data.
- Response variable-It is a variable whose values are predicted by explanatory variables.
- Standard deviation-It is a measure of how to spread out the data-set is from its mean value.
- Type I error-It can be defined as rejecting a null hypothesis that is actually true.
- Type II error-It is defined as not rejecting a null hypothesis that is actually false.
- Confounding variable-It is a variable that has not been accounted for in the study design and affects the response variable.
- Odds ratio-It is a measure of association between two variables.
- Causation-It is the relationship between an effect and its cause.
- Critical region-It is the range of values that contain unlikely or impossible values under the null hypothesis.
To Reject or Not to Reject a Null Hypothesis?
Whether you should accept or reject the null hypothesis depends on your experiment—scientists who design tests use a statistical significance level to determine the null hypothesis’s fate.
A critical value is used to determine whether or not a null hypothesis can be rejected. The critical value of the test is determined by three factors: significance level, number of tails, and slope of the line.
Whether you can reject the null hypothesis or not is determined by using a test of significance. This statistical test measures the number of standard deviations between your observed and expected values to determine whether or not to reject the null hypothesis.
Why do We Need a Null Hypothesis?
Are you wondering why researchers waste all their time on something that may not be true? The following are some reasons why a null hypothesis is necessary for a research study.
- It is a starting point-When you conduct a study, there must be a clear path to analysis and conclusions. Every experiment needs to have an objective and an approach on how to get it done.
- A null hypothesis establishes clear roles-You may find the role of the “lead investigator” confusing when researching independently.
- It provides a starting point for investigation
- Null hypotheses make it easier to read research literature-When you are reading similar studies, all of them should have the same foundation, the null hypothesis.
- A null hypothesis often leads to further research-This is why you see many studies that may only provide the null hypothesis.
Types of Null Hypotheses
The four main types of null hypotheses are exact, inexact, simple, and composite hypotheses.
Exact hypothesis
This type defines the parameter’s exact value. For example, a null hypothesis may assume that the average height of a certain group is 5 feet and 9 inches.
Inexact hypothesis
This type provides a range for the parameter’s value but not an exact figure. An example would be to assume that people who drink red wine have slightly lower blood pressure than those who do not drink red wine.
Simple hypothesis
It defines the population distribution specifically, and the sample size determines the sampling distribution.
Composite hypothesis
It doesn’t give specifics on the population distribution but instead provides specific information about the variability of samples.
Take Away
In science, a null hypothesis is the initial proposed solution to a problem. The null hypotheses provide scientists with information that can help them form an experiment and determine if their conclusions are accurate.
Hypothesis testing puts the null and alternative hypotheses back to back to determine which one offers a better explanation. The null hypothesis states that there is no relationship between two variables, while the alternative hypothesis provides information about whether or not there is a relationship.
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