Lecoutre, Lecoutre, and Poitevineau (2010) have provided sophisticated grounding for "p[subscript rep]." Computing it precisely appears, fortunately, no more difficult than doing so approximately. Their analysis will help move predictive inference into the mainstream. Iverson, Wagenmakers, and Lee (2010) have also validated "p[subscript rep]" as a boundary condition in a model-averaging approach. I argue that the boundary is good; that the proper place for their assignment of subjective priors is to update personal belief; and that such assignment has no place in the evaluation of evidence, on which priors should be flat. Maraun and Gabriel (2010) have provided clarification, context, and critique of the derivation of "p[subscript rep]." They concluded that prediction can never be precise without knowledge of population parameters and joint empirical distributions; that nature evolves; that a large initial effect will encourage replication attempts, which are therefore not independent of it; and that there is no substitute for a sustained program of replication attempts--all of which are prudent cautions. If statistics is to be the handmaiden of science and policy, however, rather than history, it ineluctably must speak to the future. The posterior predictive distribution is an underexploited tool in the analyst's kit that will serve that end, and "p[subscript rep]" is but one valid implementation of it.