Statistical modeling is at the core of many scientific disciplines. Although a vast literature exists on the process of statistical modeling, on good practices, and on abuses of statistical models, the literature lacks the discussion of a key component: the distinction between modeling for explanatory purposes and modeling for predictive purposes. This omission exacts considerable cost in terms of advancing scientific research in many fields. The goal of this paper is to highlight the distinction between the two modeling paths and to discuss key differences in the statistical modeling process.