Misconceptions, Misuses, and Misinterpretations of P Values and Significance Testing

J Bone Joint Surg Am. 2017 Sep 20;99(18):1598-1603. doi: 10.2106/JBJS.16.01314.


The interpretation and reporting of p values and significance testing in biomedical research are fraught with misconceptions and inaccuracies. Publications of peer-reviewed research in orthopaedics are not immune to such problems. The American Statistical Association (ASA) recently published an official statement on the use, misuse, and misinterpretation of statistical testing and p values in applied research. The ASA statement discussed 6 principles: (1) "P-values can indicate how incompatible the data are with a specified statistical model." (2) "P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone." (3) "Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold." (4) "Proper inference requires full reporting and transparency." (5) "A p-value, or statistical significance, does not measure the size of an effect or the importance of a result." (6) "By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis." The purpose of this article was to discuss these principles. We make several recommendations for moving forward: (1) Authors should avoid statements such as "statistically significant" or "statistically nonsignificant." (2) Investigators should report the magnitude of effect of all outcomes together with the appropriate measure of precision or variation. (3) Orthopaedic residents and surgeons must be educated in biostatistics, the ASA principles, and clinical epidemiology. (4) Journal editors and reviewers need to be familiar with and enforce the ASA principles.

MeSH terms

  • Biomedical Research / standards*
  • Data Interpretation, Statistical*
  • Humans
  • Orthopedics*
  • Probability
  • Research Design / standards