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What are Bonferroni corrected t-tests?
The Bonferroni test, also known as “Bonferroni correction” or “Bonferroni adjustment” suggests that the p-value for each test must be equal to its alpha divided by the number of tests performed. The test is named for the Italian mathematician who developed it, Carlo Emilio Bonferroni (1892–1960).
What is the Bonferroni correction and why do we use it when conducting multiple t-tests on the same criterion variable?
Purpose: The Bonferroni correction adjusts probability (p) values because of the increased risk of a type I error when making multiple statistical tests.
What does Bonferroni say about number of t tests?
The Bonferroni correction says, “if any of the t-tests in the list has p≤.05/ (number of t-tests in the list), then the hypothesis is rejected”. What is important is the number of tests, not how many of them are reported to have p≤.05.
Which is an example of the Bonferroni correction?
For example, if we perform three statistical tests at once and wish to use α = .05 for each test, the Bonferroni Correction tell us that we should use αnew = .01667. Thus, we should only reject the null hypothesis of each individual test if the p-value of the test is less than .01667.
How to correct for multiple comparisons in Bonferroni?
The estimated differences are the coefficients of interaction factor variables. Following regress, a test statement with the mtest () option will correct for multiple comparisons. Below, I show how to buildup the test statement for an arbitrary number of group levels.
Which is more rigorous the Tukey or Bonferroni method?
When used as a post hoc test after ANOVA, the Bonferroni method uses thresholds based on the t-distribution; the Bonferroni method is more rigorous than the Tukey test, which tolerates type I errors, and more generous than the very conservative Scheffé’s method.