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Given how much I’ve blogged about the 2016 ASA p-value statement, the 2019 Executive Editor’s editorial in The American Statistician (TAS), the 2020 ASA (President’s) Task Force, and the various casualties of the related teeth pulling, I thought I should say something about the recent article by Robert Matthews in Significance (March 2026): “The ASA p-value statement 10 years on: An event of statistical significance?” He begins: “Ten years ago this month, the American Statistical Association (ASA) took the unprecedented step of issuing a statement on one of the most controversial issues in statistics: the use and abuse of p-values.” The Statement is here, 2016 ASA Statement on P-Values and Statistical Significance [1]. The Executive director of the ASA, Ronald Wasserstein, invited me to be a ”philosophical observer” at the meeting which gave rise to the 2016 statement. Although the 2016 ASA statement wasn’t radically controversial, at least as compared to the 2019 Executive Editor’s editorial, which I’ll get to in a minute, it was met with critical reactions on all sides. Stephen Senn provides a figure displaying relationships between reactions. Here’s how Matthews’ article begins: Continue reading
The concept of a test’s power, originating in Neyman-Pearson’s early work, by and large, is a pre-data concept for purposes of specifying a test (notably, determining worthwhile sample size), and choosing between tests. In some papers, however, Neyman lists a third goal for power: to interpret test results post data much in the spirit of what is often called “power analysis”. This is to determine the discrepancy from a null hypothesis that may be ruled out, given nonsignificant results. One example is in a paper “
Error Statistics Philosophy 