[From the preprint “Abandoning statistical significance is both sensible and practical” by Valentin Amrhein, Andrew Gelman, Sander Greenland, and Blakely McShane, available at PeerJ Preprints]

“Dr Ioannidis writes against our proposals to abandon statistical significance…”

“…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.”

“We also disagree that abandoning statistical significance will reduce science to “a state of statistical anarchy.” Indeed, the journal Epidemiology banned statistical significance in 1990 and is today recognized as a leader in the field.”

“The replication crisis in science is not the product of the publication of unreliable findings. … Rather, the replication crisis has arisen because unreliable findings are presented as reliable.”

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