Controlling the False Discovery Rate¶
Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B, 57(1), 289-300.
Cited by¶
5 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Clustering Illusion
- Genomics confronts the same structure at massive scale: a genome-wide scan tests hundreds of thousands of variants for association with a trait, and because so many candidates are examined, the sample-size-perversity invariant guarantees that some will exceed any naive threshold by noise; the corrective is multiple-testing correction (Bonferroni or false-discovery-rate control), which is the build-the-null-and-compare discipline applied to the maximum over many comparisons.
This sourceIntroduces false-discovery-rate control for multiple testing — the build-the-null-and-compare discipline applied to maxima over many candidates (genome-wide scans, motif finding).
- Genomics confronts the same structure at massive scale: a genome-wide scan tests hundreds of thousands of variants for association with a trait, and because so many candidates are examined, the sample-size-perversity invariant guarantees that some will exceed any naive threshold by noise; the corrective is multiple-testing correction (Bonferroni or false-discovery-rate control), which is the build-the-null-and-compare discipline applied to the maximum over many comparisons.
- Effect Size
This sourceBibliography-only (tier C); linked.
- Multiple Comparisons Correction
- The distinguishing commitment is that inferential conclusions must account for the full testing context and family structure, not just a single test's p-value in isolation
This sourceBenjamini Hochberg false discovery rate FDR control multiple testing procedure step-up.
- The distinguishing commitment is that inferential conclusions must account for the full testing context and family structure, not just a single test's p-value in isolation
- Texas Sharpshooter Fallacy
- Suppose a researcher tests \(m = 1000\) independent associations (say, 1000 candidate genes against a trait), each at significance level \(\alpha = 0.05\).
This sourceIntroduces false-discovery-rate control for the multiple-comparisons problem; underpins the multiplicity correction in large-scale (e.g. genome-wide) testing.
- Suppose a researcher tests \(m = 1000\) independent associations (say, 1000 candidate genes against a trait), each at significance level \(\alpha = 0.05\).
Domain-specific¶
Verification¶
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