Post hoc test for Friedman test I am running a Friedman test for my non-Gaussian data, on MATLAB. However, I am confused about the post hoc test to choose. Two concerns:
(1) MATLAB's multcompare function's default would be Tukey HSD, but I read in multiple sites saying that it's only for parametric test (?). Link to the MATLAB guide on multcompare. If this is the case then it's quite silly that MATLAB suggested to do multiple comparison following Friedman. Other options would be Bonferroni method, Dunn and Sidák’s approach, Fisher's least significant difference procedure or Scheffé's S procedure
(2) Would it be somehow better to do a Wilcoxon test and correct to the number of hypothesis (eg in my case, 6) using Bonferonni's method?
Please if anyone could enlighten me with this
 A: So:


*

*Fisher LSD, Tukey HSD and Scheffe test are all parametric, so this is not an option for you

*p-value corrections like Bonferroni, Dunn-Sidak and many others (Holm, Benjamini-Hochberg, ...) always can be used

*beware of Bonferroni, it is the most conservative correction (it can dump your type-I error far below 5%)


If you are looking for post-hoc test after Friedman test, Conover and Nemenyi procedures may be options to consider.
Here you can read about them.
Two notes:


*

*this is a tutorial for R pacjage called PMCMR not for MATLAB (but contains some formulas to give you some insight into those tests)

*if you switch to R, you sholud use PMCMRplus package as PMCMR is outdated and no longer maintained

A: If you believe your data do not satisfy the assumptions of the parametric F test for ANOVA, and decide to use Friedman procedure, it would not make sense to use a parametric approach for the post-hoc tests. Tukey HSD, Dunn and Sidák and Fisher LSD are, at least in their original version, based on the same assumptions as the F test, so they are not advised after using Friedman test and could lead to conclusions that are in total contradiction with Friedman.
If you don't have too many treatments (or levels or conditions) and therefore not too many post hoc tests, the easiest way would be to do be to compute separately for each pair of treatments a new Freidman test, and multiply its p-value by the number of pairs tested. This provides you a Bonferonni correction that is coherent with the omnibus Friedman test.
