Severe Testing: Beyond (frequentist) Performance and (Bayesian) Probabilism
High-profile failures of replication in the social and biological sciences underwrite a minimal requirement of evidence: If little or nothing has been done to rule out flaws in inferring a claim, then it has not passed a severe test. A claim is severely tested to the extent it has been subjected to and passes a test that probably would have found flaws, were they present. The goal of highly well tested claims differs from that of highly probable ones, explaining why experts so often disagree about statistical reforms. The concept of severe testing applies beyond formal testing to estimation, prediction, and problem solving more generally. I will consider implications of this perspective for (a) relating statistical inference and scientific inference, and (b) understanding and getting beyond today’s statistics wars.
Bio
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.