Please enable JavaScript to view this site.

This guide is for an old version of Prism. Browse the latest version or update Prism

Correct for multiple comparisons by controlling the False Discovery Rate

Controlling the False Discovery Rate (FDR) is a great method for coping with multiple comparisons. Prism 6 offered this as part of the multiple t test analysis. Prism now offers it in three places.

As a followup to ANOVA

Prism has long offered multiple comparisons tests after ANOVA to control the Type I error rate for the family of comparisons. Now Prism 7 lets you use an alternative strategy for multiple comparisons following ANOVA (one-, two- or three-way): controlling the False Discovery Rate (FDR).

 

Multiple comparisons for P values computed elsewhere

Prism offers an analysis to analyze a stack of P values computed elsewhere. You enter a set of P values into a column and choose this analysis. Prism graphs the rank of each P value vs. the P value itself. This is a standard way to visualize the distribution of a set of P values.

Which P values are small enough so the corresponding finding should be flagged as "statistically significant"? Choose to control the False Discovery Rate or the familywise Type I error rate using the method of Bonferroni, Sidak or Holm.

clip0095

 

Multiple t tests, one per row

Prism 6 introduced an analysis to run multiple t tests, one per row. If you choose the method that controls the Prism 7 also reported q values (also called adjusted P values) for each comparison.

 

Three algorithms for using the FDR method

Whenever you choose to use the FDR approach to decide which P values are small enough to be a "discovery", Prism lets you choose one of three methods for controlling the FDR.

clip0094

 

© 1995-2019 GraphPad Software, LLC. All rights reserved.