Neal, I’m not sure whether it’s directly related, but I differentiate between mechanistic and statistical models. Mechanistic models being an explanation of the underlying drivers (e.g. laws in hard science — or the wildest speculative views of how something works). Statistical models being numerical relationships between measurements. Mechanistic models can be validated to a given probability of correctness, by statistically sane tests. Statistical models can also indicate possible mechanisms.
The conceptual models you describe often require a bit of sharpening if they’re to be used by >1 person.
The specific danger that I encounter is that a statistical model has been created with insufficient rigour, leading to a misunderstanding of how a phenomenon works. The biggest non-obvious issue is that the numbers going into the statistical model don’t represent what anyone thinks they do.
What do you think?