Chapter

An Error-Statistical Philosophy of Evidence

Deborah G. Mayo

in The Nature of Scientific Evidence

Published by University of Chicago Press

Published in print October 2004 | ISBN: 9780226789552
Published online February 2013 | e-ISBN: 9780226789583 | DOI: http://dx.doi.org/10.7208/chicago/9780226789583.003.0004
An Error-Statistical Philosophy of Evidence

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Error-statistical methods in science have been the subject of enormous criticism, giving rise to the popular statistical “reform” movement and bolstering subjective Bayesian philosophy of science. Is it possible to have a general account of scientific evidence and inference that shows how we learn from experiment despite uncertainty and error? One way that philosophers have attempted to affirmatively answer this question is to erect accounts of scientific inference or testing where appealing to probabilistic or statistical ideas would accommodate the uncertainties and error. Leading attempts take the form of rules or logics relating evidence (or evidence statements) and hypotheses by measures of confirmation, support, or probability. We can call such accounts logics of evidential relationship (or E-R logics). This chapter reflects on these logics of evidence and compares them with error statistics. It then considers measures of fit vs. fit combined with error probabilities, what we really need in a philosophy of evidence, criticisms of Neyman-Pearson statistics and their sources, the behavioral-decision model of Neyman-Pearson tests, and the roles of statistical models and methods in statistical inference.

Keywords: error statistics; Neyman-Pearson tests; statistical models; statistical inference; scientific evidence; logics of evidential relationship; measures of fit; error probabilities; philosophy; behavioral-decision model

Chapter.  16606 words.  Illustrated.

Subjects: Animal Pathology and Diseases

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