![]() ![]() Holm's method has more power than the Bonferroni or Tukey methods (4). ![]() Although usually attributed to Holm, in fact this method was first described explicitly by Ryan (3) so is sometimes called the Ryan-Holm step down method.The method is also called the Holm step-down method.The Holm multiple comparison test cannot compute confidence intervals for the difference between means.The Holm multiple comparison test can calculate multiplicity adjusted P values, if you request them (2).The input to the Holm method is a list of P values, so it is not restricted to use as a followup test to ANOVA.As part of the analysis that performs many t tests at once.The alternative is to use the Holm-Šídák method, which has more power but which doesn't compute confidence intervals. In this case, we recommend the Bonferroni method as it can compute confidence intervals for each comparison. If your data are only entered into two columns, you may choose to compare the two values at each row (or with two rows to compare the two values within each column). Instead, we suggest choosing the Tukey test if you want to compute confidence intervals for every comparison or the Holm-Šídák test if you don't. In this case, the situation is much like one-way ANOVA: the Bonferroni test is offered because it is easy to understand, but we don't recommend it. If you have three or more columns of data, you may choose to to compare means within each row (or with three or more rows to compare means within each column). Instead, choose the Tukey test if you want to compute confidence intervals for every comparison or the Holm-Šídák test if you don't. Prism also lets you choose Bonferroni tests when comparing every mean with every other mean. This makes sense when you are comparing selected pairs of means, with the selection based on experimental design. As a multiple corrections test following ANOVA.Prism can perform the Holm multiple comparisons test as part of several analyses: ![]()
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