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Austere Realism and the Worldly Assumptions of Inferential Statistics

Published online by Cambridge University Press:  28 February 2022

J.D. Trout
J.D. Trout*
Affiliation:
Loyola University of Chicago
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Extract

Inferential statistical tests—such as analysis of variance, t-tests, chi-square and Wilcoxin signed ranks—now constitute a principal class of methods for the testing of scientific hypotheses. In particular, inferential statistics (when properly applied) are normally understood, for better or worse, as warranting a theoretical (typically causal) inference from an observed sample to an unobserved part of the population. These methods have been applied with great effect in domains such as population genetics, mechanics, cognitive, perceptual and social psychology, economics, and sociology, and it is not often that scientists in these fields eschew causal explanation in favor of the statement of brute correlations among properties. However, the appropriateness of a particular statistical test, and the reliability of the inference from sample to population, are subject to the application of certain statistical principles and concepts.

Type
Part V. Realism and its Guises
Information
PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , Volume 1994 , Issue 1: Volume One: Contributed Papers 1994 , 1994 , pp. 190 - 199
Copyright
Copyright © 1994 by the Philosophy of Science Association

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Footnotes

1

For comments on this paper, thanks are owed to Paul Moser.

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