To Explain or to Predict?

Details To Explain or to Predict? To Explain or to Predict?

2011-01-05

arxiv.org/abs/1101.0891v1

Statistical Science 2010, Vol. 25, No. 3, 289-310

Statistics

Methodology

Published in at http://dx.doi.org/10.1214/10-STS330 the Statistical Science (http://www.imstat.org/sts/) by the Institute of M

Scientific paper

10.1214/10-STS330

Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.

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