Thanks for the ASA statement link. Yes, obviously the whole notion of a hard threshold is a bit nonsensical to begin with, but as the ASA statement says "Pragm

Thanks for the ASA statement link. Yes, obviously the whole notion of a hard threshold is a bit nonsensical to begin with, but as the ASA statement says "Pragmatic considerations often require binary, yes-no decisions". There doesn't seem any way around that. People face decisions like, shall we continue to fund this investigation? Yes/no. Should we recommend lifestyle changes to the public? Yes/no. It doesn't make sense to try and map a P value into a budget, for example. At some point you need a threshold (and likewise for effect size and other things). Of course at some level there is fuzzyness, which is why I said if a paper is mostly reporting 0.049 values then ... and I didn't specify what, exactly because the conclusion should be something like "fuzzily suspicious and should look closer". So it's fine for the ASA to complain about statistical significance leading to "distortion of the scientific process", but their proposed alternative can be boiled down to doing more peer review, as most of the things they tell researchers to look at are things researchers will never conclude in the negative about their own results, like study design (because if they did they wouldn't have got to the point of calculating a P value to begin with). Finally, I disagree when you say "That is false". Scientists aren't going to stop using statistical significance thresholds because they do need to make binary decisions at some point, and dropping the threshold they currently use by 10x would immediately yield major improvements in replicability and robustness. Anyway, put P-hacking to one side if you dislike that discussion, it's fine. It's not actually the thing that bothers me the most when I read papers. A big gap between the prose summaries and what the data [analysis] actually shows is a much more common source of distortion, IMO.