Deborah Mayo (Virginia Tech & LSE): “The Statistical Replication Crisis: Paradoxes and Scapegoats”

Abstract: Mounting failures of replication in the social and biological sciences give a practical spin to statistical foundations in the form of the question: How can we attain reliability when Big Data methods make illicit cherry-picking and significance seeking so easy? Researchers, professional societies, and journals are increasingly getting serious about methodological reforms to restore scientific integrity – some are quite welcome (e.g., preregistration), while others are quite radical. Recently, the American Statistical Association convened members from differing tribes of frequentists, Bayesians, and likelihoodists to codify misuses of P-values. Largely overlooked are the philosophical presuppositions of both criticisms and proposed reforms. Paradoxically, alternative replacement methods may enable rather than reveal illicit inferences due to cherry-picking, multiple testing, and other biasing selection effects. Popular appeals to “diagnostic testing” that aim to improve replication rates may (unintentionally) permit the howlers and cookbook statistics we are at pains to root out. Without a better understanding of the philosophical issues, we can expect the latest reforms to fail.

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