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

• Frederick Dorey
In Brief (By Invitation Only)

## Background

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...

## Keywords

Null Hypothesis Prior Belief Bayesian Statistic Scientific Validity Multivariate Statistical Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

## Supplementary material

Supplementary material 1 (MP4 27522 kb)

## References

1. 1.
Dorey F. The p value: what is it and what does it tell you? Clin Orthop Relat Res. 2010;468:2297–2298.
2. 2.
Dorey FJ. In brief: statistics in brief: confidence intervals: what is the real result in the target population? Clin Orthop Relat Res. 2010;468:3137–3138.
3. 3.
Dorey FJ. Statistics in brief: statistical power: what is it and when should it be used? Clin Orthop Relat Res. 2011;469:619–620.
4. 4.
Greenhalgh T. How to Read a Paper: The Basics of Evidence-Based Medicine. Chichester, UK: BMJ Books; 2007.Google Scholar
5. 5.
Jolles BM, Martin E. In brief: statistics in brief: study designs in orthopaedic clinical research. Clin Orthop Relat Res. 2011;469:909–913.
6. 6.
Lambert J. Statistics in brief: how to assess bias in clinical studies? Clin Orthop Relat Res. 2011;469:1794–1796.
7. 7.
Motulsky H. Intuitive Biostatistics. New York, NY: Oxford University Press; 1995.Google Scholar
8. 8.
Oxford Center for Evidence-Based Medicine. Available at: http://www.cebm.net/index.aspx?o=5653. Accessed August 9, 2011.