One way is with modeling assumptions. Recall that the T test was invented to be used in controlled beer fermentation experiments, which I think should fall into your "hard" science category. But the T test has strict distributional assumptions, without which the test is not valid and its results are meaningless.
Another way is that we can improve our modeling assumptions, or at least our interpretation of modeled results, by doing basic descriptive data analysis before experimenting. Unless you are extremely starved for data, you will very quickly notice if your data follows something like a power law distribution. Then you can adjust your experimentation and modeling accordingly, even if it's just tempering your ability to draw conclusions for a randomized controlled trial.
Yet another way is with replication of experiments. We are not talking about observational data here, so these experiments can be replicated. A power-law distribution, again, would be noticeable here, and we'd see a high variance in results across individual experiments.
> You don't see a difference between actually necessarily controlling a cause by causal intervention, vs., "somehow, hopefully, on average" possible relevant causes are controlled for?
I don't, because "actually necessarily controlling a cause by causal intervention" does not exist in real life. It's just a matter of how much the data varies around the average case, which tends to be greater in the social sciences than in the natural sciences... on average, with plenty of variation around that average.
One way is with modeling assumptions. Recall that the T test was invented to be used in controlled beer fermentation experiments, which I think should fall into your "hard" science category. But the T test has strict distributional assumptions, without which the test is not valid and its results are meaningless.
Another way is that we can improve our modeling assumptions, or at least our interpretation of modeled results, by doing basic descriptive data analysis before experimenting. Unless you are extremely starved for data, you will very quickly notice if your data follows something like a power law distribution. Then you can adjust your experimentation and modeling accordingly, even if it's just tempering your ability to draw conclusions for a randomized controlled trial.
Yet another way is with replication of experiments. We are not talking about observational data here, so these experiments can be replicated. A power-law distribution, again, would be noticeable here, and we'd see a high variance in results across individual experiments.
> You don't see a difference between actually necessarily controlling a cause by causal intervention, vs., "somehow, hopefully, on average" possible relevant causes are controlled for?
I don't, because "actually necessarily controlling a cause by causal intervention" does not exist in real life. It's just a matter of how much the data varies around the average case, which tends to be greater in the social sciences than in the natural sciences... on average, with plenty of variation around that average.