Point taken, and I agree. I'll try to better formulate what I meant:

Some theories are developed using data about the system you want to study. E.g., past climate data.

And some theories are developed using data about other systems. Either similar but causally unrelated ones (e.g., greenhouse effect in an actual greenhouse), or models which are so simplified that there's a serious worry they may be too simplified to apply to the original system (e.g., black-body radiation). They also have the advantage that if they work on the system you want to study, then they let you explain it in terms of other things which you already understand.

On an abstract Bayesian level, they're all the same; we don't compartmentalize data about past climate from data about the optical properties of gasses. But for humans who work in different fields the difference matters.