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RUTGERS UNIVERSITY DEPARTMENT OF STATISTICS AND BIOSTATISTICS www.stat.rutgers.edu

Seminar θSpeaker:     Professor Deborah Mayo, Virginia Tech

Title:           Probing with Severity: Beyond Bayesian Probabilism and Frequentist Performance

Time:          3:20 – 4:20pm, Wednesday, December 3, 2014 Place:         552 Hill Center

ABSTRACT

Probing with Severity: Beyond Bayesian Probabilism and Frequentist Performance Getting beyond today’s most pressing controversies revolving around statistical methods, I argue, requires scrutinizing their underlying statistical philosophies.Two main philosophies about the roles of probability in statistical inference are probabilism and performance (in the long-run). The first assumes that we need a method of assigning probabilities to hypotheses; the second assumes that the main function of statistical method is to control long-run performance. I offer a third goal: controlling and evaluating the probativeness of methods. An inductive inference, in this conception, takes the form of inferring hypotheses to the extent that they have been well or severely tested. A report of poorly tested claims must also be part of an adequate inference. I develop a statistical philosophy in which error probabilities of methods may be used to evaluate and control the stringency or severity of tests. I then show how the “severe testing” philosophy clarifies and avoids familiar criticisms and abuses of significance tests and cognate methods (e.g., confidence intervals). Severity may be threatened in three main ways: fallacies of statistical tests, unwarranted links between statistical and substantive claims, and violations of model assumptions.