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In this post, I consider the questions posed for my (October 9) Neyman Seminar by Philip Stark, Distinguished Professor of Statistics at UC Berkeley. We didn’t directly deal with them during the panel discussion following my talk, and I find some of them a bit surprising. (Other panelist’s questions are here).
Philip Stark asks:
When and how did Statistics lose its way and become (largely) a mechanical way to bless results rather than a serious attempt to avoid fooling ourselves and others?
- To what extent have statisticians been complicit in the corruption of Statistics?
- Are there any clear turning points where things got noticeably worse?
- Is this a problem of statistics instruction ((a) teaching methodology rather than teaching how to answer scientific questions, (b) deemphasizing assumptions, (c) encouraging mechanical calculations and ignoring the interpretation of those calculations), (d) of disciplinary myopia (to publish in the literature of particular disciplines, you are required to use inappropriate methods), (e) of moral hazard (statisticians are often funded on scientific projects and have a strong incentive to do whatever it takes to bless “discoveries”), or something else?
- What can academic statisticians do to help get the train back on the tracks? Can you point to good examples?
These are important and highly provocative questions! To a large extent, Stark and other statisticians would be the ones to address them. As an outsider, and as a philosopher of science, I will merely analyze these questions. and in so doing raise some questions about them. That’s Part I of this post. In Part II, I will list some of Stark’s replies to #5 in his (2018) joint paper with Andrea Saltelli “Cargo-cult statistics and scientific crisis”. (The full paper is relevant for #1-4 as well.) Continue reading
Error Statistics Philosophy 