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When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing

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  • Rubin, Mark

    (The University of Newcastle, Australia)

Abstract

Scientists often adjust their significance threshold (alpha level) during null hypothesis significance testing in order to take into account multiple testing and multiple comparisons. This alpha adjustment has become particularly relevant in the context of the replication crisis in science. The present article considers the conditions in which this alpha adjustment is appropriate and the conditions in which it is inappropriate. A distinction is drawn between three types of multiple testing: disjunction testing, conjunction testing, and individual testing. It is argued that alpha adjustment is only appropriate in the case of disjunction testing, in which at least one test result must be significant in order to reject the associated joint null hypothesis. Alpha adjustment is inappropriate in the case of conjunction testing, in which all relevant results must be significant in order to reject the joint null hypothesis. Alpha adjustment is also inappropriate in the case of individual testing, in which each individual result must be significant in order to reject each associated individual null hypothesis. The conditions under which each of these three types of multiple testing is warranted are examined. It is concluded that researchers should not automatically (mindlessly) assume that alpha adjustment is necessary during multiple testing. Illustrations are provided in relation to joint studywise hypotheses and joint multiway ANOVAwise hypotheses.

Suggested Citation

  • Rubin, Mark, 2021. "When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing," MetaArXiv tj6pm, Center for Open Science.
  • Handle: RePEc:osf:metaar:tj6pm
    DOI: 10.31219/osf.io/tj6pm
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    References listed on IDEAS

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    1. Daniel Yekutieli & Anat Reiner‐Benaim & Yoav Benjamini & Gregory I. Elmer & Neri Kafkafi & Noah E. Letwin & Norman H. Lee, 2006. "Approaches to multiplicity issues in complex research in microarray analysis," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(4), pages 414-437, November.
    2. Shuyan Mei & Ali Karimnezhad & Marie Forest & David R Bickel & Celia M T Greenwood, 2017. "The performance of a new local false discovery rate method on tests of association between coronary artery disease (CAD) and genome-wide genetic variants," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-14, September.
    3. Kim, Jaehoon & Kim, Sangsin, 2015. "2012년 국회법 개정의 효과 연구 [A Study on the Effect of the 2012 National Assembly Act Amendment]," KDI Research Monographs, Korea Development Institute (KDI), volume 127, number v:2015-03(k):y:2015:p:1-1.
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    2. Alec Brandon & Justin E. Holz & Andrew Simon & Haruka Uchida, 2023. "Minimum Wages and Racial Discrimination in Hiring: Evidence from a Field Experiment," Upjohn Working Papers 23-389, W.E. Upjohn Institute for Employment Research.
    3. Sander Greenland, 2023. "Divergence versus decision P‐values: A distinction worth making in theory and keeping in practice: Or, how divergence P‐values measure evidence even when decision P‐values do not," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 54-88, March.
    4. Geva, Sharon & Fulman, Nir & Ben-Elia, Eran, 2022. "Getting the prices right: Drivers' cruising choices in a serious parking game," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 54-75.

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