Andrew Gelman, a pioneer of the methodology of Bayesian statistics (cited in original article; he doesn't call himself a Bayesian in the philosophical sense) re

Andrew Gelman, a pioneer of the methodology of Bayesian statistics (cited in original article; he doesn't call himself a Bayesian in the philosophical sense) responded to the ASA statement so: http://www.stat.columbia.edu/~gelman/research/published/asa_pvalues.pdf http://www.stat.columbia.edu/~gelman/research/published/asa_... His conclusion there is that scientific institutions are not accepting, as they need to be, of systematic uncertainty. Elsewhere he says that scientists who are not statisticians should concentrate on gathering quality data: noise in data often leads to spurious point estimates: https://statmodeling.stat.columbia.edu/2017/02/11/measurement-error-replication-crisis/ https://statmodeling.stat.columbia.edu/2017/02/11/measuremen... In the first link, Gelman talks of the "garden of forking paths": this is essentially a generalisation of the idea of p-hacking to recognise that perfectly honest researchers will not conduct unbiased analyses because of the myriad of parameters that estimates depend on. The solution is to move away from summarising results through point estimates to construct statistical models where you can explore the space of possible analyses; there has been a revolution in the techniques for doing so through the application of Monte-Carlo Markov-chain techniques to construct posterior distributions.