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Can you do multiple t tests instead of Anova?
Even when more than two groups are compared, some researchers erroneously apply the t test by implementing multiple t tests on multiple pairs of means. For a comparison of more than two group means the one-way analysis of variance (ANOVA) is the appropriate method instead of the t test.
Why is it better to use ANOVA than multiple t tests?
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. An ANOVA controls for these errors so that the Type I error remains at 5% and you can be more confident that any statistically significant result you find is not just running lots of tests.
When to do a Bonferroni correction in ANOVA?
1) It is said if you are comparing multiple sample means using ANOVA and once you find there is some significant difference then you can do a post hoc analysis by doing pairwise comparison. But now you don’t have to actually do a Bonferroni correction.
When to use the Bonferroni post hoc test?
The Bonferroni Method. The Bonferroni post-hoc test should be used when you have a set of planned comparisons you would like to make beforehand. For example, suppose we have three groups – A, B, C – and we know ahead of time that we’re only interested in the following comparisons: μ A = μ B; μ B = μ C
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.
When to use the Bonferroni correction-PubMed?
Abstract Purpose: The Bonferroni correction adjusts probability (p) values because of the increased risk of a type I error when making multiple statistical tests.