[cjglo]

Could be wrong here, but in physics and most natural sciences, you don’t throw away your model if you have one experiment against it.

Usually isn’t it looking for an experiment that proves it and is repeatable?

If I discover a new element in one experiment, the results are published.

After publication, many labs will try to repeat and its not taken away if one can’t do it. Only if all can’t and it casts doubt on whether I did it in the first place.

[Jensson]

Scientific method? Models are disproved, not proved. Do data scientists not know about science? People usually understand how science works here on HN, but not in this thread.

Example of a test that invalidated our old theory of gravity and validated Einsteins claims:

https://en.wikipedia.org/wiki/Eddington_experiment

This is how science is done. But apparently not data science.

[caddemon]

Models can also be knowingly/intentionally incomplete, which necessarily introduces noise that you are not controlling for. Meaning you have to use statistics, and there isn't really a concept of prove or disprove in the true sense of those words.

Maybe very far in the future there will be models of human biology that are as robust as classical physics, but right now there is such a large amount that is not understood, it's simply not feasible. A drug could work for one person and not another for reasons beyond the realistic scope of the original development hypothesis. It requires a probabilistic view to make any sort of statement about the efficacy then.

I suppose you could argue these models are just wrong and thus trivially disproven, but I don't think that's a productive framing. I doubt any biologist or doctor would claim they have anywhere near a complete model of how their specialty works. That doesn't mean a particular model isn't useful or isn't the best we currently have to work with.

Plus maybe the third best model will actually turn out to explain a separate puzzle piece in an eventual better model. Mechanistic models in biology aren't always well done in practice, but it's certainly not binary either.