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Why should we not perform multiple t-tests when comparing multiple groups?
Why not compare groups with multiple t-tests? Every time you conduct a t-test there is a chance that you will make a Type I error. By running two t-tests on the same data you will have increased your chance of “making a mistake” to 10%.
What is multiple t tests?
•The multiple t test (and nonparametric) analysis is designed to analyze data from the Grouped format data table. Data for each individual t test should be entered onto a single row of the data table. Two columns (each with an appropriate number of subcolumns) represent the two groups being compared.
Why are paired t tests more powerful?
Paired t-tests are considered more powerful than unpaired t-tests because using the same participants or item eliminates variation between the samples that could be caused by anything other than what’s being tested.
How to perform paired t test for multiple groups?
Here is the scenario: I would like to perform a paired t-test of sorts on multiple groups. Instead of before and after conditions, there are multiple conditions (ex: treatment 1, treatment 2, treatment 3 etc). Each subject appears in each ‘group’.
What happens when you run two t tests?
By running two t-tests on the same data you will have increased your chance of “making a mistake” to 10%. The formula for determining the new error rate for multiple t-tests is not as simple as multiplying 5% by the number of tests.
When to use a t test in a pairwise comparison?
When to use a t-test A t-test can only be used when comparing the means of two groups (a.k.a. pairwise comparison). If you want to compare more than two groups, or if you want to do multiple pairwise comparisons, use an ANOVA test or a post-hoc test.
Is there a nonparametric alternative to the t test?
If your data do not fit these assumptions, you can try a nonparametric alternative to the t-test, such as the Wilcoxon Signed-Rank test for data with unequal variances. What type of t-test should I use?