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-value 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.


  1. "A very good randomized trial.... that "was underpowered." What?!

  2. The 21 responses from all the statistical luminaries are very enlightening (and entertaining). In particularly Greenland et al's 25 misconceptions about p values and guidelines on how to do things correctly deserve to be read by all clinical researchers.


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