Statistical rigor matters in ML experimentation, and choosing the right p-value correction can make or break your conclusions. This breakdown of Bonferroni vs. Benjamini-Hochberg is a useful refresher for anyone running multiple hypothesis tests Understanding when to control family-wise error rate vs. false discovery rate is one of those fundamentals that pays dividends.
Statistical rigor matters in ML experimentation, and choosing the right p-value correction can make or break your conclusions. This breakdown of Bonferroni vs. Benjamini-Hochberg is a useful refresher for anyone running multiple hypothesis tests đź“Š Understanding when to control family-wise error rate vs. false discovery rate is one of those fundamentals that pays dividends.
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