ArticleBiometrical journal. Biometrische Zeitschrift2026
When to Adjust for Multiple Testing: A Unifying Guiding Principle.
Article in Biometrical journal. Biometrische Zeitschrift, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Mental Health and Health-Related Behavioral Patterns Surrounding a Major International Sporting Event: An Observational Study.Diseases (Basel, Switzerland) · 2026Article
- Procrastination among US college students: Prevalence and impact relative to common health and life issues.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Most original articles published in the medical literature report the results of multiple statistical tests. In a few simple cases, there is agreement on whether to adjust for the number of performed tests. For many cases encountered in practice, however, this is less clear, and the recommendations in the literature are contradictory, along different dimensions, or otherwise confusing. This lack of clear guidance may impair the conduct and interpretation of analyses, and encourage questionable research practices, ultimately jeopardizing the credibility of medical research. In this article, we refine, illustrate, and discuss a unifying guiding principle to assist both statisticians and applied researchers in deciding whether to adjust for multiple testing and, if so, over which set of tests. The principle is that multiple testing should be adjusted for if and only if authors, when reporting and interpreting their findings, put more emphasis on results of one or several of the tests because of their small p-value(s). We relate this principle to previously proposed rules and show how it can guide and clarify the choice of adjustment strategies in three complex multiple testing settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.