GraphPad Prism 11 Statistics Guide - Three approaches to dealing with multiple comparisons

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Navigation: PRINCIPLES OF STATISTICS > Multiple comparisons

Three approaches to dealing with multiple comparisons

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Approach 1: Don't correct for multiple comparisons

When it makes sense to not correct for multiple comparisons

Example: Planned comparisons

Fisher's Least Significant Difference (LSD)

Approach 2: Control the Type I error rate for the family of comparisons

What it means to control the Type I error for a family

Multiplicity adjusted P values

Bonferroni and Šídák methods

The Holm-Šídák method

Tukey and Dunnett methods

Dunn's multiple comparisons after nonparametric ANOVA

Newman-Keuls method

Approach 3: Control the False Discovery Rate (FDR)

What it means to control the FDR

Key facts about controlling the FDR

Pros and cons of the three methods used to control the FDR

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