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Model Selection, Simplicity, and Scientific Inference

Published online by Cambridge University Press:  01 January 2022

Wayne C. Myrvold  and
William L. Harper
William L. Harper*
Affiliation:
University of Western Ontario
*
Send requests for reprints to the authors, Department of Philosophy, University of Western Ontario, London, ON N6A 3K7, Canada; Myrvold: [email protected]; Harper: [email protected].
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Abstract

The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, agreeing measurements of causally relevant parameters contributes to the evidential support of the theory. This is illustrated by Newton's argument from orbital phenomena to the inverse-square law of gravitation.

Type
Research Article
Information
Philosophy of Science , Volume 69 , Issue S3 , September 2002 , pp. S135 - S149
Copyright
Copyright © The Philosophy of Science Association

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