Login / Signup

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 SorkinMark J ManaryPaul A M SmeetsAmanda J MacFarlaneArnie AstrupRonald L PrigeonBeth B HogansJack OdleTeresa A DavisKatherine L TuckerChristopher P DugganDeirdre K Tobias
Published in: The American journal of clinical nutrition (2021)
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.
Keyphrases
  • physical activity
  • adverse drug
  • systematic review
  • healthcare
  • depressive symptoms
  • emergency department
  • randomized controlled trial
  • meta analyses