It's well known what p-values show. But they are, in practice, used as a gatekeeping mechanism in academic journals in many fields (including mine). Worse, getting p<0.05 is informally seen as a measure of practical significance, rather than simply as one statistical test amongst many passed.

So yes, it is something you learn in introductory quantitative methods classes. But I don't think most researchers understand just how much it matters.

Also, a key R package for producing regression tables of coefficients for journal articles is called 'stargazer'. Given the unwarranted focus of many readers on those indicia of 'significant' results, I think it's well named.

I currently have the opposite problem. Given that I work with very large online datasets (N=1M or so) everything, including the random noise, is statistically significant to p<0.05. It really is effect sizes or busy at that point.