What Multiple tests is
Multiple tests refer to a technique used to identify statistically significant results from among a set of tests. This technique is used to reduce the likelihood of making a Type I error, which is the false rejection of a true null hypothesis. Multiple tests can be used to make a decision on whether or not to reject the null hypothesis.
Steps for Multiple Tests:
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Define the research question and the null hypothesis.
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Determine the alpha level (e.g., 0.05) and the Type I error rate that is acceptable.
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Determine the sample size and the number of tests to be conducted.
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Conduct the tests and compare the results to the predetermined alpha level.
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If the results of at least one test are statistically significant, then reject the null hypothesis.
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If the results of all tests are not statistically significant, then fail to reject the null hypothesis.
Examples
- Multiple Regression Analysis: Testing multiple independent variables to see which are related to a dependent variable.
- ANOVA: Testing multiple groups to see if there is a statistically significant difference between them.
- Factor Analysis: Testing multiple variables to identify which are related to a common factor.
- Chi-Square Test: Testing multiple categorical variables to see if there is a relationship between them.
- Power Analysis: Estimating the sample size necessary to detect a difference between multiple groups.