There are a large number multiple testing p-value correction methods. e.g.:
bonferroni : one-step correction
sidak : one-step correction
holm-sidak : step down method using Sidak adjustments
holm : step-down method using Bonferroni adjustments
simes-hochberg : step-up method (independent)
hommel : closed method based on Simes tests (non-negative)
fdr_bh : Benjamini/Hochberg (non-negative)
fdr_by : Benjamini/Yekutieli (negative)
fdr_tsbh : two stage fdr correction (non-negative)
fdr_tsbky : two stage fdr correction (non-negative)
(based on https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html)
I have found a lot of pages that explain the methods individually (and why corrections are needed) but I have not found an overview of when to use which method e.g. a comparison table or even better a decision flow diagram as it exists for machine learning methods.
Any ideas? How do I decide which multiple testing correction I should apply?
Disclose: I already posted this in data science but even with a bounty no useful answer was given. Maybe here is more appropriate?