[animefan]

I don't think that's a fair characterization of the issue. p-hacking is done almost exclusively by people who completely understand the definition and interpretation of p-values. However these people are also under a lot of pressure to produce positive results (and sometimes, results in a specific direction) and this biases their thinking.

The problem is not that people are not aware of the issue of testing multiple hypotheses. The problem is that (1) it's hard to say exactly what your hypothesis is before you've even looked at the data, and (2) it's hard to determine if people are choosing parameters for p-hacking or simply making choices based on their best judgement.

>Furthermore, p-values were designed to deal with experimental data. If you're doing an observational study, perhaps you should use statistical tools designed for that purpose.

This is simply wrong. p-values are equally relevant in both cases. E.g. I can use p-values to reject the hypothesis that consuming saturated fats is uncorrelated with weight gain, amongst the general population. It sounds like you are reaching beyond your actual expertise in statistics.