To Explain or to Predict?

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Statistical Science2010, Vol. 25, No. 3, 289 310 Institute of Mathematical Statistics, 2010To Explain or to Predict? Galit modeling is a powerful tool for developing and testingtheories by way of causal explanation, prediction, and description. In manydisciplines there is near-exclusive use of statistical modeling for causal ex-planation and the assumption that models with high explanatory power areinherently of high predictive power. Conflation between explanation and pre-diction is common, yet the distinction must be understood for progressingscientific knowledge. While this distinction has been recognized in the phi-losophy of science, the statistical literature lacks a thorough discussion of themany differences that arise in the process of modeling for an explanatory ver-sus a predictive goal.

nearly absent in many scientific fields as a tool for de-veloping theory. One possible reason is the statistical training of nonstatistician researchers. A look at many introductory statistics textbooks reveals very little in the way of prediction. Another reason is that prediction is often considered unscientific. Berk (2008) wrote, “In

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