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Is it possible to get a situation where, in a multiple-testing scenario, for some individual tests, the Benjamini&Yukatieli (2001)-correction ends up being more stringent than Bonferroni? I.e. that for some tests, $$ p_{\text{Bonferroni}} < p_{BY} $$

I seem to have encountered this with data and while I have no conceptual problem with it (the procedures are after all quite different), but would have intuitively said that with every correction any p-value can be at most as large as with Bonferroni.

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It turns out that indeed, this can and does trivially happen, as tested on two different implementations in Python, from statsmodels and pingouin, with only two tests and p-values of 0.01 and 0.1.

from pingouin import multicomp as pingouin_multicomp
from statsmodels.stats.multitest import multipletests as statsmodels_multicomp
pvals = [0.01, 0.1]

With this,

pingouin_multicomp(pvals, method="bonf")[1] -> array([0.02, 0.2 ])
pingouin_multicomp(pvals, method="fdr_by")[1] -> array([0.03, 0.15])
statsmodels_multicomp(pvals, method="fdr_by")[1] -> array([0.03, 0.15])

Unless there is some deep conceptual mistake I made, it would appear that the smaller value gets corrected more.

Interestingly, this does not seem to happen with the original Benjamini & Hochberg (1995) procedure (as suggested by the method outlined in the post linked by Christoph Hanck). If anyone wants to outline the difference, I'll shift the accept; for now this is at least what seems to be confirmation that I'm not crazy.

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