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Deborah Mayo (Virginia Tech): “Severe Testing: A Conjecture Passes a Severe Test Only if a Refutation Would Probably Have Occurred if it’s False”
3 June 2019, 2:00 pm – 3:30 pm
Abstract: High-profile failures of replication in the social and biological sciences underwrite a minimal requirement of evidence: If a conjecture is retained when little or nothing has been done that would have refuted it, then it has not passed a severe test. This minimal severe-testing requirement leads to reformulating statistical significance tests (and related methods) to avoid familiar criticisms and abuses. The goal of highly well tested claims differs from that of highly probable ones, explaining why experts so often disagree about statistical reforms.
Viewing statistical inference as severe testing–whether or not you accept it–offers a key to understand and get beyond the statistics wars.
Deborah G. Mayo is Professor Emerita in the Department of Philosophy at Virginia Tech and is a visiting professor at the London School of Economics and Political Science, Centre for the Philosophy of Natural and Social Science. She is the author of Error and the Growth of Experimental Knowledge (Chicago, 1996), which won the 1998 Lakatos Prize awarded to the most outstanding contribution to the philosophy of science during the previous six years. She co-edited Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science (CUP, 2010) with Aris Spanos, and has published widely in the philosophy of science, statistics, and experimental inference. Her most recent book is Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (CUP, 2018). She will co-direct (with Aris Spanos) a Summer Seminar on Philosophy of Statistics at Virginia Tech, with 15 participating philosophy and social science faculty and post docs, July 28-August 11, 2019.