Seminars 2009 — Abstracts

Friday, January 23

Speaker: Galit Shmueli, University of Maryland

Title: Explanatory Modeling vs. Predictive Modeling in Scientific Research

Abstract: Explanatory models are designed for testing hypotheses that specify how and why certain empirical phenomena occur. Predictive models are aimed at predicting new observations with high accuracy. An age-old debate in philosophy of science deals with the difference between predictive and explanatory goals. In mainstream statistical research, however, the distinction between explanatory and predictive modeling is mostly overlooked, and there is a near-exclusive focus on explanatory methodology. This focus has permeated into empirical research in many fields such as information systems, economics and in general,  the social sciences. We discuss the issue from a practical statistical modeling perspective. Our premise is that (1) both explanatory and predictive statistical models are essential for advancing scientific research; and (2) the different goals lead to key differences at each step of the modeling process. In this talk we discuss the statistical divergences between modeling for an explanatory goal and modeling for a predictive goal. In particular, we analyze each step of the statistical modeling process (from data collection to model use) and describe the different statistical components and issues that arise in explanatory modeling vs. predictive modeling. We close with a discussion of implications of this work to the general scientific community and to the field of statistics.