A response to critiques of ‘The reproducibility of research and the misinterpretation of p-values’

I proposed (8, 1, 3) that p values should be supplemented by an estimate of the false positive risk (FPR). FPR was defined as the probability that, if you claim that there is a real effect on the basis of p value from a single unbiased experiment, that you will be mistaken and the result has occurred by chance. This is a Bayesian quantity and that means that there is an infinitude of ways to calculate it. My choice of a way to estimate FPR was, therefore, arbitrary. I maintain that it is a reasonable way, and has the advantage of being mathematically simpler than other proposals and easier to understand than other methods. This might make it more easily accepted by users. As always, not every statistician agrees. This paper is a response to a critique of my 2017 paper (1) by Arandjelovic (2)

Abstract licence: CC BY

1. 5.

   An investigation of the false discovery rate and the misinterpretation of p -values · 2014

2. 11.
   Corrected

   Why Most Published Research Findings Are False · 2005

3. 20.
   Corrected

   The reproducibility of research and the misinterpretation of p -values · 2017

19 more references, whose wording the paper’s licence keeps private.

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