 If proponents of the statistical approach are in general agreement that simplicity should be cashed out in terms of number of parameters, there is less unanimity over what the goal of simplicity principles ought to be. This is partly because the goal is often not made explicit. (An analogous issue arises in the case of Occam’s Razor. ‘Entities are not to be multiplied beyond necessity.’ But necessity for what, exactly?) Forster distinguishes two potential goals of model selection, namely probable truth and predictive accuracy, and claims that these are importantly distinct (Forster 2001, p. 95). Forster argues that predictive accuracy tends to be what scientists care about most. They care less about the probability of an hypothesis being exactly right than they do about it having a high degree of accuracy.