Thanks, although I must admit I'm having some trouble following your logic. IIUC, you're pointing out that when examining the empirical data gathered during an

Thanks, although I must admit I'm having some trouble following your logic. IIUC, you're pointing out that when examining the empirical data gathered during an experiment, it's often possible to find some identifiable subset of the data that are consistent with a refined version of the original hypothesis. E.g., maybe jellybeans in general don't correlate with acne, but green ones do. Assuming that the experiment is part of a larger effort to build or refine some model, I don't see the problem. Suppose that someone refrained from p-hacking during the first run of that experiment. IIUC, they'd look at the experimental results, and wonder if they had missed some "X" factor. So they might conject that jellybean color was relevant, and rerun the experiment with the hypothesis, "consumption of jellybeans, but only of a particular color, correlates with acne." And (assuming sample sizes were big enough), the data gathered during that second experiment would likely confirm that green jellybeans correlate with acne. But what's the point of having run that second experiment, when they could have just reached the same conclusion by testing additional hypotheses from the first experiment's data? It seems like regardless of whether you just ran one experiment and did "p-hacking", or instead ran a follow-on experiment, you end up with the same refinements to the model you're working on.