Divide the significance level by the number of comparisons to be tested. Set a significance level for the error rate familywise. This lesson explains how to use an F ratio with analysis of variance to test statistical hypotheses represented by planned comparisons.īonferroni's correction is an adjustment to the significance level used to evaluate the statistical significance of an individual comparison. This lesson describes how the probability of committing a Type I error is affected by the number of comparisons tested. It explains how to represent a statistical hypothesis mathematically by a comparison.Īnd it explains how to compute the sum of squares for a comparison. This lesson defines an ordinary comparison. If you don't know these things, review the following lessons: Type I error is affected by the number of comparisons tested.Īnd you should know how to use an F ratio to test multiple comparisons. You should understand how the probability of committing a You should be able to compute the sum of squares associated with a comparison. You should know how to represent a statistical hypothesis mathematically by a comparison. Prerequisites: This lesson assumes familiarity with multiple comparisons for follow-testing in ANOVA. The lesson is all about the Bonferroni correction - what it is, why it is needed, when to use it, and how to implement it. The Bonferroni correction (aka, Bonferroni adjustment, Bonferroni test, Bonferroni method) is way to control error rate familywise with experiments that test multiple comparisons. Home > ANOVA tutorial > This page Bonferroni Correction
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