One of the most influential lectures I ever caught in grad school was Ryan Gutenkunst’s talk on sloppy models. Model sloppiness, first developed by Jim Sethna and his students and collaborators, is a ubiquitous phenomenon in mathematical models for the natural sciences. Often, especially in biology, a model of a system of interest will have many more parameters to be fit than observations to fit them with: a scenario like eighty parameters and six data points is not uncommon. Standard theory tells you that you are doomed here. But if you ignore standard theory and plow ahead, you typically find that the (under)fit model will predict future experiments perfectly well. How could that possibly work?