A hugely influential aphorism from the statistician George E. P. Box is, "all models are wrong, but some are useful." For example, it is often useful to model a random variable as normally distributed even though huge quantities of data will almost always give you the statistical power to reject normality. In my field of finance, asset pricing theorists argue that asset pricing models should be judged on whether their theories help you understand the data, not whether a model is or isn't statistically rejected. I could go on and on with theorists invoking Box.
Which line of thought in the philosophy of science does Box's statement (or perhaps Box's broader writings) most align with?
(Philosophy is not my field, but is a key philosophical distinction whether models are regarded as false because they simplify or whether scientific realism is rejected altogether?)