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Benjamini-Hochberg procedure fails FDR control for correlated Gaussian tests

By PulseAugur Editorial · [1 sources] ·

A new paper demonstrates that the Benjamini--Hochberg procedure can fail to control the false discovery rate (FDR) for correlated two-sided Gaussian tests. Researchers constructed a factor model where, at a 0.01 significance level, the FDR exceeded 0.0104 for a large number of hypotheses. This finding disproves a long-standing conjecture and was generated with the assistance of GPT-5.6 Pro, then meticulously verified by the author. AI

IMPACT Challenges assumptions in statistical testing, potentially impacting AI model evaluation and research reproducibility.

RANK_REASON Academic paper presenting novel theoretical findings that disprove a long-standing conjecture. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Benjamini-Hochberg procedure fails FDR control for correlated Gaussian tests

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Academic paper presenting novel theoretical findings that disprove a long-standing conjecture. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Edgar Dobriban ·

    The Benjamini--Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests

    arXiv:2607.12208v1 Announce Type: cross Abstract: We show that the Benjamini--Hochberg procedure can fail to control the false discovery rate (FDR) at its nominal level for correlated two-sided Gaussian $p$-values. We construct a factor model for which, at level $\alpha=0.01$, a …

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