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.

ogy acceptance in IS. The authors then proceed to state multiple causal hy-potheses (denoted H1,H2,...in Figure 1, right panel), justifying the merits for each hypothesis and ground-ing it in theory. The research hypotheses are given in terms of theoretical constructs rather than measurable variables. Unlike measurable variables, constructs are

  Acceptance