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Extending the p-plot: Heuristics for multiple testing

  • IAN ABRAMSON (a1), TANYA WOLFSON (a2), THOMAS D. MARCOTTE (a2), IGOR GRANT (a2) (a3) and THE HNRC GROUP (a2)...

Abstract

In the problem of large-scale multiple testing the p-plot is a graphically based competitor to the notoriously weak Bonferroni method. The p-plot is less stringent and more revealing in that it gives a gauge of how many hypotheses are decidedly false. The method is elucidated and extended here: the bootstrap reveals bias and sampling error in the usual point estimates, a bootstrap-based confidence interval for the gauge is given, as well as two acceptably powerful blanket tests of significance. Guidelines for use are given, and interpretational pitfalls pointed out, in the discussion of a case study linking premortem neuropsychological and postmortem neuropathologic data in an HIV cohort study. (JINS, 1999, 5, 510–517)

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Corresponding author

Reprint requests to: Tanya Wolfson, HIV Neurobehavioral Research Center, 2760 5th Avenue Ste 200, San Diego CA 92103. E-mail: twolfson@ucsd.edu

Keywords

Extending the p-plot: Heuristics for multiple testing

  • IAN ABRAMSON (a1), TANYA WOLFSON (a2), THOMAS D. MARCOTTE (a2), IGOR GRANT (a2) (a3) and THE HNRC GROUP (a2)...

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