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

Mark Rubin
- 06 Jul 2021 - 
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TLDR
This article 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.
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.

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References
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Journal ArticleDOI

Controlling the false discovery rate: a practical and powerful approach to multiple testing

TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI

What's wrong with Bonferroni adjustments

TL;DR: This paper advances the view, widely held by epidemiologists, that Bonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inference.
Journal ArticleDOI

No adjustments are needed for multiple comparisons.

Kenneth J. Rothman
- 01 Jan 1990 - 
TL;DR: A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature.
Book

Simultaneous Statistical Inference

TL;DR: In this article, the authors presented a case of two means regression method for the family error rate, which was used to estimate the probability of a family having a nonzero family error.
Journal ArticleDOI

Rectangular Confidence Regions for the Means of Multivariate Normal Distributions

TL;DR: For rectangular confidence regions for the mean values of multivariate normal distributions, this paper proved that a confidence region constructed for independent coordinates is, at the same time, a conservative confidence region for any case of dependent coordinates.
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Trending Questions (2)
When to Adjust Alpha During Multiple Testing: A Consideration of Disjunction, Conjunction, and Individual Testing?

Alpha adjustment is appropriate in disjunction testing but inappropriate in conjunction and individual testing. Researchers should not automatically assume alpha adjustment is necessary during multiple testing.

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

Alpha adjustment is appropriate for disjunction testing but not for conjunction testing or individual testing.