Banning p-values makes sense to me, as they force you to declare an effect as either significant or not significant, rather than looking at the preponderance of

Banning p-values makes sense to me, as they force you to declare an effect as either significant or not significant, rather than looking at the preponderance of the evidence and building knowledge over multiple experiments. It also leads us to focus on statistical uncertainty at the expense of all of the other kinds of uncertainty researchers are faced with: do I have the theory to back this up, am I actually measuring what I am trying to measure, is this coherent with other findings in the field? I do think the editors might be right when they say banning p-values will make the quality of the research go up, not down. But if you read the original editorial, up at http://www.tandfonline.com/doi/pdf/10.1080/01973533.2015.1012991 http://www.tandfonline.com/doi/pdf/10.1080/01973533.2015.101..., you can see that they also reject confidence intervals and Bayesian reasoning with uniform priors (which is really the same thing) without providing any guidance at all on better procedures. I fear that will just lead readers to try and guess the reliability of the data themselves, or worse, interpret the sample statistics as numbers without any associated uncertainty. So they're doing away with poor statistical procedures, but at what cost? It's like that old joke: we've found a 100% reliable cure for cancer – bombing the planet until everybody's dead.