Idealizations, deliberate distortions introduced into scientific theories and models, are commonplace in science. This has led to a puzzle in epistemology and philosophy of science: How could a deliberately false claim or representation lead to the epistemic successes of science? In answering this question philosophers have been single-focused on explaining how and why idealizations are successful. But surely some idealizations fail. Sullivan proposes that if we ask a slightly different question, whether a particular idealization is successful, then that not only gives insight into idealization failure, but will make us realize that our theories of idealization need revision. In this talk Sullivan considers idealizations in physics, computation, and machine learning.