A large amount of published results that are wrong is definitely something science can live with: we have to trade off Type I against Type II. But we should val

A large amount of published results that are wrong is definitely something science can live with: we have to trade off Type I against Type II. But we should value accuracy: if we report something as being very, very unlikely if chance was at play, and it turns out that in fact (1) it'd be very likely even if the null hypothesis holds and (2) in fact even if P(D|H0) is low, P(H0|D) might be high... then what's the point in writing up all those fancy statistical analyses anyway? At that point significance testing becomes more of religious ritual and should either be discarded entirely or be amended.