Our Data Mining (different field but somewhat related) professor had this quote "All models are wrong, but some models are useful" on the first day of university.
You can build an extremely complicated model that is not useful, where a simpler one might suffice.
I've heard that line referred to as Box's Razor. It's definitely the right heuristic, but it's interesting to see that even if your model is right, you're still in trouble if it's too complex. This is a sort of bias/variance tradeoff.
You can build an extremely complicated model that is not useful, where a simpler one might suffice.