 Dawes’s original point was not that improper linear models outperform proper linear models in terms of accuracy, but rather that they are more efficient and (often) close approximations of proper linear models. “The statistical model may integrate the information in an optimal manner”, Dawes observed, “but it is always the individual …who chooses variables” (Dawes 1979: 573). Moreover, Dawes argued that it takes human judgment to know the direction of influence between predictor variables and target variables, which includes the knowledge of how to numerically code those variables to make this direction clear. Recent advances in machine learning chip away at Dawes’s claims about the unique role of human judgment, and results from Gigerenzer’s ABC Group about unit-weight tallying outperforming linear regression in out-of-sample prediction tasks with small samples is an instance of improper linear models outperforming proper linear models (Czerlinski, Gigerenzer, &amp; Goldstein 1999). Nevertheless, Dawes’s general observation about the relative importance of variable selection over variable weighting stands (Katsikopoulos, Schooler, &amp; Hertwig 2010).