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Comment by uniqueuid | Hacker News Reader

The real underlying problem is that in your case, genetic variants are not accounted for. As soon as you include these crucial moderating covariates, it‘s absolutely possible to find true effects even for (rather) small samples (one out of a hundred is really to few for any reasonable design unless it‘s longitudinal)

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Anything in health sciences has millions of variants not accounted for, that also interact between themselves so you'd need to account for every combination of them.

> As soon as you include these crucial moderating covariates

Yes, but this is not usually done.

It is also not easy if you have many potential covariates! Because statistically, you want a complete (explaining all effects) but parsimonious (using as few predictors as possible) model. Yet you by definition don‘t know the true underlying causal structure. So one needs to guess which covariates are useful. There are also no statistical tools that can, given your data, explain whether the model sufficiently explains the causal phenomenon, because statistics cannot tell you about potentially missing confounders.

A cool, interesting, horrible problem to have :)

And it's usually discouraged by regulators because it can lead to p-hacking. I.e., with a good enough choice of control I can get anything down to 5%

The fundamental problem is the lack of embrace of causal inference techniques - i.e., the choice of covariates/confounders is on itself a scientific problem that needs to be handled with love

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