Post-hoc tests after Friedman test In Statistical Comparisons of Classifiers over Multiple Data Sets (p. 12) Janez Demsar suggests to use the Bonferroni-Dunn test as a post-hoc test after the null hypothesis of an initial Friedman test was rejected.
Questions:

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*Friedman test is non-parametric. May one use a parametric test as a post-hoc test (e.g., paired t-test with Bonferroni adjustment)?

*Is the Wilcoxon signed-rank test with Bonferroni adjustment also an appropriate post-hoc test in this case?

*The paper does not suggest pairwise comparisons of all groups if one wants to test a new algorithm against existing baselines. Instead, one should only compare the new algorithm (control) to the existing ones. How would Bonferroni adjustment work in this case? Still by  dividing by the number of tests that were conducted?


None of the tests mentioned (t-test, Wilcoxon signed rank test, Dunn test) match the Friedman's test, with how they handle the data or the assumptions involved.  There are post-hoc test specifically for Friedman's test. The best source I can suggest is the documentation for the R package PMCMRplus cran.r-project.org/web/packages/PMCMRplus/PMCMRplus.pdf, and look at the functions that start with frd.  These tests have names like Conover, Miller, Nemenyi, Siegel and Castellan, but be cautious because there may be distinct tests attributed to these authors.
You also might reconsider using Friedman's test.  There may be better options for your purpose, like Quade test, ordinal regression, or aligned ranks transformation anova.
