>I see the problem as being with the entire hypothesis testing framework, with the idea that we learn by rejecting straw-man (or, as Dave Krantz charmingly said once, “straw-person”) null hypotheses, and with the binary true/false attitude which leads people to believe that, once a result is judged statistically significant (by any standard) and published in a good journal, that it deserves the presumption of belief.

That is too simply brushing off decades of statistical work. While over reliance on p values is a problem, especially if the p values reflect garbage models which don't properly fit the data. We can't just throw out theory because we don't like it. Setting up proper statistical testing is still a powerful tool for experimental data.