Curve fitting, the reliability of inductive inference, and the error-statistical approach

Philosophy of Science 74 (5):1046-1066 (2007)
Abstract
The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest' curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical modeling aspects of the problem. Using goodness-of-fit as the primary criterion for ‘best', the mathematical approximation perspective undermines the reliability of inference objective by giving rise to selection rules which pay insufficient attention to ‘accounting for the regularities in the data'. A more appropriate framework is offered by the error-statistical approach, where (i) statistical adequacy provides the criterion for assessing when a curve captures the regularities in the data adequately, and (ii) the relevant error probabilities can be used to assess the reliability of inductive inference. Broadly speaking, the fittest curve (statistically adequate) is not determined by the smallness if its residuals, tempered by simplicity or other pragmatic criteria, but by the nonsystematic (e.g. white noise) nature of its residuals. The advocated error-statistical arguments are illustrated by comparing the Kepler and Ptolemaic models on empirical grounds. ‡I am grateful to Deborah Mayo and Clark Glymour for many valuable suggestions and comments on an earlier draft of the paper; estimating the Ptolemaic model was the result of Glymour's prompting and encouragement. †To contact the author, please write to: Department of Economics, Virginia Tech 3019 Pamplin Hall (0316), Blacksburg, VA 24061; e-mail: aris@vt.edu.
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DOI 10.1086/525643
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References found in this work BETA
Theory and Evidence.Clark Glymour - 1980 - Princeton University Press.
Error and the Growth of Experimental Knowledge.Deborah G. Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
The Foundations of Scientific Inference.Wesley C. Salmon - 1966 - University of Pittsburgh Press.

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Citations of this work BETA
Is Frequentist Testing Vulnerable to the Base-Rate Fallacy?Aris Spanos - 2010 - Philosophy of Science 77 (4):565-583.
Causal Models and Evidential Pluralism in Econometrics.Alessio Moneta & Federica Russo - 2014 - Journal of Economic Methodology 21 (1):54-76.

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