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Statistical modeling is a powerful tool for developing and testing theoriesby way of causal explanation, prediction, and description. In many disciplinesthere is near-exclusive use of statistical modeling for causal explanation andthe assumption that models with high explanatory power are inherently of highpredictive power. Conflation between explanation and prediction is common, yetthe distinction must be understood for progressing scientific knowledge. Whilethis distinction has been recognized in the philosophy of science, thestatistical literature lacks a thorough discussion of the many differences thatarise in the process of modeling for an explanatory versus a predictive goal.The purpose of this article is to clarify the distinction between explanatoryand predictive modeling, to discuss its sources, and to reveal the practicalimplications of the distinction to each step in the modeling process.

To Explain or to Predict?