A guide for authors and readers of the American Society for Nutrition Journals on the proper use of P values and strategies that promote transparency and improve research reproducibility

John D. Sorkin*, Mark Manary, Paul A.M. Smeets, Amanda J. Macfarlane, Arne Astrup, Ronald L. Prigeon, Beth B. Hogans, Jack Odle, Teresa A. Davis, Katherine L. Tucker, Christopher P. Duggan, Deirdre K. Tobias

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)

Abstract

Two questions regarding the scientific literature have become grist for public discussion: 1) what place should P values have in reporting the results of studies? 2) How should the perceived difficulty in replicating the results reported in published studies be addressed? We consider these questions to be 2 sides of the same coin; failing to address them can lead to an incomplete or incorrect message being sent to the reader. If P values (which are derived from the estimate of the effect size and a measure of the precision of the estimate of the effect) are used improperly, for example reporting only significant findings, or reporting P values without account for multiple comparisons, or failing to indicate the number of tests performed, the scientific record can be biased. Moreover, if there is a lack of transparency in the conduct of a study and reporting of study results, it will not be possible to repeat a study in a manner that allows inferences from the original study to be reproduced or to design and conduct a different experiment whose aim is to confirm the original study's findings. The goal of this article is to discuss how P values can be used in a manner that is consistent with the scientific method, and to increase transparency and reproducibility in the conduct and analysis of nutrition research.

Original languageEnglish
Pages (from-to)1280-1285
Number of pages6
JournalAmerican Journal of Clinical Nutrition
Volume114
Issue number4
DOIs
Publication statusPublished - 1 Oct 2021

Keywords

  • P value
  • reliability
  • reproducibility
  • strategies
  • transparency

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