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Achieving Statistical Significance with Covariates and without Transparency

Gabriel Lenz and Alexander Sahn
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Gabriel Lenz: UC Berkeley

No s42ba, MetaArXiv from Center for Open Science

Abstract: How often do articles depend on suppression effects for their findings? How often do they disclose this fact? By suppression effects, we mean covariate-induced increases in effect sizes. Researchers generally scrutinize suppression effects as they want reassurance that researchers have a strong explanation for effect size increases, especially when the statistical significance of the key finding depends on them. In this article, we find that 30-40% of observational articles in a leading journal depend on suppression effects for statistical significance. Although suppression effects are of course potentially justifiable---to address suppressor variables---none of the articles justifies or discloses them. These findings may point to a hole in the review process: journals are accepting articles that depend on suppression effects without readers, reviewers, or editors being made aware.

Date: 2017-04-07
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DOI: 10.31219/

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