And that's without even considering the scientists who cheat the p-values, which is actually just a tiny part of the problem with p-values.
What's needed is a better understanding of empirical conditional statistics and the application of that understanding to the degign of experiments and the analysis of the results produced therefrom.
Many of the more data-centric fields did away with p-values long ago in favor of far more robust approaches to establishing the value of a paper. Look at papers in the data science field. Using p-values in a paper there would get you laughed out of the industry fast.
Only problem that p-values ever solved was allowing journal reviewers to be lazier, helping scientists to be lazier, and making writing more sensational press releases easier.