Deborah G. Mayo is professor emerita in the Department of Philosophy at Virginia Tech. Her Error and the Growth of Experimental Knowledge won the 1998 Lakatos Prize in philosophy of science. She is a research associate at the London School of Economics: Centre for the Philosophy of Natural and Social Science (CPNSS). She co-edited (with A. Spanos) Error and Inference (2010, CUP). Her most recent book is Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP). She founded the Fund for Experimental Reasoning, Reliability and the Objectivity and Rationality of Science (E.R.R.O.R) which sponsored a 2 week summer seminar in Philosophy of Statistics in 2019 for 15 faculty in philosophy, psychology, statistics, law and computer science (co-directed with A. Spanos). She publishes widely in philosophy of science, statistics, and philosophy of experiment. She blogs at errorstatistics.com and phil-stat-wars.com.
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Talk: "Statistical Inference as Severe Testing: How it (Still) Gets You Beyond the Statistics Wars"
Abstract: High-profile failures of replication in the social and biological sciences underwrites 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. Many methods being advocated to reform statistical practice, I argue, do not stand up to severe scrutiny and are even in tension with successful strategies to improve replication. The minimal severe-testing requirement leads to reformulating statistical significance tests (and related methods) to avoid familiar criticisms and abuses. Viewing statistical inference as severe testing–whether or not you accept it–(still) offers a key to understand and get beyond today’s statistics wars.