<p>Scientists routinely draw inferences about target systems by deriving them from highly idealized models, and their derivations tend to explicitly rely on assumptions which are clearly false of their targets. Such practices would appear to be unsound, and their justification presents a challenge in model epistemology. To meet this challenge, we develop a method, ‘Soundness Analysis’, for decomposing an idealized model into its true and false aspects in order to determine whether a model’s true aspects can serve as premises in a sound argument for the model’s conclusion. We illustrate the method on the Hoff-Stiglitz model from economics, and we argue that Soundness Analysis has epistemic advantages over prevailing de-idealization strategies. </p>