Blog | Tuesday, April 5, 2016
Moving beyond the 0.05 P value
One of my common refrains in research conference and here is that the misuse of P values has negative public health consequences, a phenomenon I call “death by P value.” Of course my level of frustration with P values pales in comparison to what well-trained statisticians must feel. This week, the American Statistical Association Board of Directors led by Ronald Wasserstein released a Statement on Statistical Significance and P values which include six principles on the use and interpretation of P values. These are:
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 passes a specific threshold.
4. Proper inference requires full reporting and transparency. A P value, or statistical significance, does not measure the size of an effect or the importance of a result.
5. By itself, a P value does not provide a good measure of evidence regarding a model or hypothesis.
In addition to the ASA statement I highly recommend the coverage in FiveThirtyEight and Retraction Watch's interview of Professor Wasserstein. We often talk about the post-antibiotic era but even more important for public health is that researchers and journals happily embrace the post P=0.05 era.
Eli N. Perencevich, MD, ACP Member, is an infectious disease physician and epidemiologist in Iowa City, Iowa, who studies methods to halt the spread of resistant bacteria in our hospitals (including novel ways to get everyone to wash their hands). This post originally appeared at the blog Controversies in Hospital Infection Prevention.