My background is physics, and I used to think that, but now working as a data scientist that has to deal with human data, I've come to think the opposite. The signal to noise ratio in human data is humongous, nothing is static, and there are zillion of moving parts. That means you need immense volumes of data to run well powered experiments that are designed to get at real causal factors. And there are ethics involved so you have to deal with potential early stopping in a statistically sound way. In some fields (like econ), you don't even really get to do experiments at all on most topics of interest, having to rely on the observations available.

But it turns out that it's worth applying the scientific method to these fields, so what you're left with are tough choices. To deal with these problems the way we would in a physics experiment would be prohibitively expensive, in the literal sense of prohibitive. You have to come to terms with the fact that you can only afford to get enough data that there's a non-negligible possibility of being misled. It's worth doing science here, and we can, but it's just plain hard. I didn't appreciate that before I started having to deal with it. Don't blame the subject for some practitioners' failings.