[7thaccount]

None of them I would guess.

Math is math and the hard sciences like physics, chemistry, biology,and engineering are all good assuming somebody isn't mining p-values or something like that.

The social sciences require a ton of data and simulating something like the economy with trillions of variables and bounded rationality just isn't possible. If you wanted to simulate an agent like myself you'd likely need millions of lines of probabilistic code and then you'd need millions of other agents running on a supercomputer the size of Rhode Island.

[Alpha3031]

Biomed is already too complex to simulate fully, which is why simulating is just, not really a thing that is done to find new knowledge. Heck, some quantum interactions are already (so far) computationally intractable, which is why quantum chemistry exists as a field at all, it's far from trivial. That doesn't mean the best practices developed for medicine won't help in the social sciences as well.

The issue with degrees of freedom can be mostly pinned down with preregistration, among other measures. It won't solve the problem of having more type I errors at any specific error rate the more science you do, but it does give assurances the target type I error rate is accurate.