The question isn’t if your model is true, but how wrong it is

Hill (1996) offers a useful comment that model builders–particularly in the social sciences–should keep in mind.

“A major defect of the classical view of hypothesis testing, […], is that it attempts to test only whether the model is true. This came out of the tradition in physics, where models such as Newtonian mechanics, the gas laws, fluid dynamics, and so on, come so close to being “true” in the sense of fitting (much of) the data, that one tends to neglect issues about the use of the model. However, in typical statistical problems (especially in the biological and social sciences but not exclusively so) one is almost certain a priori that the model taken literally is false in a non-trivial way, and so one is instead concerned whether the magnitude of discrepancies is sufficiently small so that the model can be employed for some specific purpose.”

A common saying in statistics, “All models are wrong, but some are useful“.