I received received a question about controlling for inflated Type I error through Bonferroni corrections in nonparametric tests. Here’s the specific question and my quick answer:
My colleague is applying non parametric (Kruskal-Wallis) to check for differences between groups. There are 12 groups and test showed that there is significant difference in the groups. However, to check which pair is significant is tedious and I’m not sure if there is comparable post-hoc test in non-parametric approach. Any resources available in hands?
My answer:
Bonferroni correction is your only option when applying non-parametric statistics (that I’m aware of). Or, actually, any test other than ANOVA.
A Bonferroni correction is actually very simple. Just take the number of comparisons you want to make, then multiply each p-value by that number. If the calculated p-value is greater than 1, round to 1.0.
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Bonferroni correction is definitely not the only option. There are many more methods to calculate multiplicity adjusted p-values, such as Hochberg, Simes, and Holm tests, to mention a few. See Analysis of clinical trials using SAS: a practical guide at http://www.google.com/books?id=G5ElnZDDm8gC&printsec=frontcover&dq=sas+clinical+trials&cd=1