The problem with ML is the same one that brought about the reproducibility crisis (RC) — believing that simply exceeding one predefined threshold for some proba

The problem with ML is the same one that brought about the reproducibility crisis (RC) — believing that simply exceeding one predefined threshold for some probabilistic metric (like correlation or p value) is 'good enough'. Of course the problem is compounded if we also fail to propose a causal mechanism and you don't try to validate it — something I see data scientists doing all too often since we seldom employ anything like Design Of Experiment practices, and the data we're working with is very rarely created by us. IMHO, the RC is a reminder to scientists that to confirm a hypothesis you need to pass more than one test, and a reminder to data scientists that we must test using more than one model.