Paper
Improving the Power of Bonferroni Adjustments under Joint Normality and Exchangeability
Authors
Caleb Hiltunen, Yeonwoo Rho
Abstract
Bonferroni's correction is a popular tool to address multiplicity but is notorious for its low power when tests are dependent. This paper proposes a practical modification of Bonferroni's correction when test statistics are jointly normal and exchangeable. This method is intuitive to practitioners and achieves higher power in sparse alternatives, as our simulations suggest. We also prove that this method successfully controls the family-wise error rate at any significance level.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.20118v1</id>\n <title>Improving the Power of Bonferroni Adjustments under Joint Normality and Exchangeability</title>\n <updated>2026-02-23T18:34:58Z</updated>\n <link href='https://arxiv.org/abs/2602.20118v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.20118v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Bonferroni's correction is a popular tool to address multiplicity but is notorious for its low power when tests are dependent. This paper proposes a practical modification of Bonferroni's correction when test statistics are jointly normal and exchangeable. This method is intuitive to practitioners and achieves higher power in sparse alternatives, as our simulations suggest. We also prove that this method successfully controls the family-wise error rate at any significance level.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='stat.ME'/>\n <published>2026-02-23T18:34:58Z</published>\n <arxiv:primary_category term='stat.ME'/>\n <author>\n <name>Caleb Hiltunen</name>\n </author>\n <author>\n <name>Yeonwoo Rho</name>\n </author>\n </entry>"
}