Comment by datsci_est_2015

I was always a fan of what a former senior colleague ingrained in me early in my career:

  - a model is primarily judged by its predictive power

A model that has no predictive utility is a bad model, but every model hits its limits in terms of predictive power. Some models have a very steep cliff in accuracy at the "edges", and some gradually lose fidelity like an out-of-focus photograph.

Most anti-science criticism I see is completely ignorant of what it really means to model a system or phenomenon: the challenges, the limitations, the end goal, the criteria for success, etc. It's sort of a statistical / scientific ignorance that's deeply cross-discipline. To perform science in a lot of ways is to engage with what it means to build a model, and in some ways, interrogate the universe.

If you're both an idiot and overconfident, it's tempting to throw your hands up and not engage with something as complex as "modeling", and just decide almost out of whimsy what the universe should truly be. But at least some people who find modeling too intellectually taxing at least admit that there are things that they'll never understand.