Nonetheless, we disagree that a statistical significance-based “filtering process is useful to avoid drowning in noise” in science and instead view such filtering as harmful. First, the implicit rule to not publish nonsignificant results biases the literature with overestimated effect sizes and encourages “hacking” to get significance. Second, nonsignificant results are often wrongly treated as zero. Third, significant results are often wrongly treated as truth rather than as the noisy estimates they are, thereby creating unrealistic expectations of replicability. Fourth, filtering on statistical significance provides no guarantee against noise. Instead, it amplifies noise because the quantity on which the filtering is based (the p-value) is itself extremely noisy and is made more so by dichotomizing it.Amrhein, Gelman, Greenland, & McShane
The controversy about significance testing continues in the pages of JAMA. The latest salvo, in the form of a letter to the editor, can be viewed in preprint form here.