Statistics in Brief: Interpretation and Use of p Values: All p Values Are Not Equal

  • Frederick DoreyEmail author
In Brief (By Invitation Only)


In a formal hypothesis testing situation, a question is frequently asked about differences between groups, and based on that question an experiment is designed, data are collected, and a statistical test is performed, usually resulting in one or more p values. The p value resulting from a hypothesis test is heuristically defined as a probability measure of how much evidence there is against the null hypothesis of the test, that is, no difference exists [1]. When the p value is small (however defined), then a decision might be made to reject the null hypothesis and accept the alternative hypothesis that a difference exists. However, in many (if not most) situations today, the reader of a medical journal has made no such prior definition of what is small, or exactly what use should be made of any given p value. Thus, despite the exact definition of what a p value means, how p values in general should be interpreted or how they should influence the readers of medical journals...


Null Hypothesis Prior Belief Bayesian Statistic Scientific Validity Multivariate Statistical Model 
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Supplementary material

Supplementary material 1 (MP4 27522 kb)


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Copyright information

© The Association of Bone and Joint Surgeons® 2011

Authors and Affiliations

  1. 1.Department of Pediatrics At Children’s Hospital Los AngelesUniversity of Southern California, Keck School of MedicineLos AngelesUSA

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