I think that the outright banning of p-values is not the right way to go. The researchers that misuse them and the reviewers that apparently don't understand them are a much bigger problem than the p-value metric itself.

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.

And the best way to do that is to eliminate the crutch.

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.

Could you point me to a starting point for understanding the new more robust approaches?

Are there journals for data science? I am not aware of any...