 Hitchcock and Sober (2004) provide an original theory of weak heuristic predictivism that is based on a particular worry about accommodation. On the assumption that data are noisy (i.e. imbued with observational error), a good theory will almost never fit the data perfectly. To construct a theory that fits the data better than a good theory should, given noisy data, is to be guilty of “overfitting”—if we know a theorist built her theory to accommodate data, we may well worry that she has overfit the data and thus constructed a flawed theory. If we know however that a theorist built her theory without access to such data, or without using it in the process of theory construction, we need not worry that overfitting that data has occurred. When such a theory goes on to make successful predictions, Hitchcock and Sober moreover argue, this provides us with evidence that the data on which the theory was initially based were not overfit in the process of constructing the theory. 