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Behavioristic, Evidentialist, and Learning Models of Statistical Testing

Published online by Cambridge University Press:  01 April 2022

Deborah G. Mayo
Deborah G. Mayo*
Affiliation:
Department of Philosophy Virginia Polytechnic Institute and State University
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Abstract

While orthodox (Neyman-Pearson) statistical tests enjoy widespread use in science, the philosophical controversy over their appropriateness for obtaining scientific knowledge remains unresolved. I shall suggest an explanation and a resolution of this controversy. The source of the controversy, I argue, is that orthodox tests are typically interpreted as rules for making optimal decisions as to how to behave–-where optimality is measured by the frequency of errors the test would commit in a long series of trials. Most philosophers of statistics, however, view the task of statistical methods as providing appropriate measures of the evidential-strength that data affords hypotheses. Since tests appropriate for the behavioral-decision task fail to provide measures of evidential-strength, philosophers of statistics claim the use of orthodox tests in science is misleading and unjustified. What critics of orthodox tests overlook, I argue, is that the primary function of statistical tests in science is neither to decide how to behave nor to assign measures of evidential strength to hypotheses. Rather, tests provide a tool for using incomplete data to learn about the process that generated it. This they do, I show, by providing a standard for distinguishing differences (between observed and hypothesized results) due to accidental or trivial errors from those due to systematic or substantively important discrepancies. I propose a reinterpretation of a commonly used orthodox test to make this learning model of tests explicit.

Type
Research Article
Information
Philosophy of Science , Volume 52 , Issue 4 , December 1985 , pp. 493 - 516
Copyright
Copyright © 1985 by the Philosophy of Science Association

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Footnotes

†

I am grateful to Ronald Giere, Norman Gilinsky, I. J. Good, Oscar Kempthorne, Henry Kyburg, and Larry Laudan for very helpful comments. I thank Jim Fetzer for first suggesting I spell out my (learning) model by contrasting it to the existing (behavioristic and evidentialist) models of statistical tests.

References

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