Is Bonferroni Admissible for Large m?

Yonggang Lu, Peter H. Westfall

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Modern methods of multiple comparisons, particularly those based on controlling the false discovery rate, are lax relative to the Bonferroni method in their assignment of significances; they are relatively more lax as m, the number of tests, increases. We point out that this laxness is based on an assumption concerning the size of the loss due to Type I errors relative to the loss due to Type II errors, and challenge the generality of this assumption, providing an alternative loss function for which the Bonferroni method is asymptotically (as m → ∞) optimal.

Original languageEnglish
Pages (from-to)51-69
Number of pages19
JournalAmerican Journal of Mathematical and Management Sciences
Issue number1-2
StatePublished - 2009


  • Asymptotic theory
  • Decision theory
  • Extreme value theory
  • Loss function
  • Multiple comparisons


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