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Misinterpretations of the ‘p value’: a brief primer for academic sports medicine
1. Steven D Stovitz1,
2. Evert Verhagen2,
3. Ian Shrier3
1. 1 Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
2. 2 Department of Public and Occupational Health Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
3. 3 Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada
1. Correspondence to Steven D Stovitz, Department of Family Medicine and Community Health, University of Minnesota, 420 Delaware Street SE, MMC 381, Minneapolis, MN 55455, USA; stovitz{at}umn.edu

## Introduction

When comparing treatment groups, the p value is a statistical measure that summarises the chance (‘p’ for probability) that one would obtain the observed result (or more extreme), if and only if, the treatment is ineffective (ie, under the assumption of the ‘null’ hypothesis). The p value does not tell us the probability that the null hypothesis is true.1 This editorial discusses how some common misinterpretations of the p value may impact sports medicine research. Although presented from a treatment standpoint, the same principles hold for causes or prevention.

## Probabilities do not translate into yes or no decisions

p Values are probabilities, yet often interpreted based on a categorical cut-off, generally at the level of 0.05 (ie, 5%). Anything below is considered a ‘statistically significant difference’ and vice versa. However, one would not change a decision to buy a lottery ticket if the chance of winning was 4.9% (p=0.049i) instead of 5.1% (p=0.051). Consider a study where 100 participants who were given an injury prevention programme had six injuries, and 100 participants …

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## Footnotes

• Competing interests None declared.

• Provenance and peer review Not commissioned; externally peer reviewed.

• i p Value calculated via two-tailed χ2 statistical test.

• ii p Value calculated via two-tailed t-test.

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