Below is an excerpt from Edwards, Lindman, and Savage (1963, pp. 236-7), on why p-value procedures continue to be dominant in the empirical sciences even after it has been repeatedly shown to be an incoherent and nonsensical statistic (note: those are my choice of words, the authors are very cordial in their commentary). The age of the article shows in numbers 1 and 2, but I think it is still valuable commentary; Numbers 3 and 4 are still highly relevant today.
From Edwards, Lindman, and Savage (1963, pp. 236-7):
If classical significance tests have rather frequently rejected true null hypotheses without real evidence, why have they survived so long and so dominated certain empirical sciences ? Four remarks seem to shed some light on this important and difficult question.
1. In principle, many of the rejections at the .05 level are based on values of the test statistic far beyond the borderline, and so correspond to almost unequivocal evidence [i.e., passing the interocular trauma test]. In practice, this argument loses much of its force. It has become customary to reject a null hypothesis at the highest significance level among the magic values, .05, .01, and .001, which the test statistic permits, rather than to choose a significance level in advance and reject all hypotheses whose test statistics fall beyond the criterion value specified by the chosen significance level. So a .05 level rejection today usually means that the test statistic was significant at the .05 level but not at the .01 level. Still, a test statistic which falls just short of the .01 level may correspond to much stronger evidence against a null hypothesis than one barely significant at the .05 level. …
2. Important rejections at the .05 or .01 levels based on test statistics which would not have been significant at higher levels are not common. Psychologists tend to run relatively large experiments, and to get very highly significant main effects. The place where .05 level rejections are most common is in testing interactions in analyses of variance—and few experimenters take those tests very seriously, unless several lines of evidence point to the same conclusions. [emphasis added]
3. Attempts to replicate a result are rather rare, so few null hypothesis rejections are subjected to an empirical check. When such a check is performed and fails, explanation of the anomaly almost always centers on experimental design, minor variations in technique, and so forth, rather than on the meaning of the statistical procedures used in the original study.
4. Classical procedures sometimes test null hypotheses that no one would believe for a moment, no matter what the data […] Testing an unbelievable null hypothesis amounts, in practice, to assigning an unreasonably large prior probability to a very small region of possible values of the true parameter. […]The frequent reluctance of empirical scientists to accept null hypotheses which their data do not classically reject suggests their appropriate skepticism about the original plausibility of these null hypotheses. [emphasis added]
Edwards, W., Lindman, H., & Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological review, 70(3), 193-242.