(This article was originally published at Doing Bayesian Data Analysis, and syndicated at StatsBlogs.)
In an opinion article [here] titled "Is the call to abandon p-values the red herring of the replicability crisis?", Victoria Savalei and Elizabeth Dunn concluded, "at present we lack empirical evidence that encouraging researchers to abandon p-values will fundamentally change the credibility and replicability of psychological research in practice. In the face of crisis, researchers should return to their core, shared value by demanding rigorous empirical evidence before instituting major changes."
I posted a comment which said in part, "people have been promoting a transition away from null hypothesis significance testing to Bayesian methods for decades, long before the recent replicability crisis made headlines. The main reasons to switch to Bayesian have little directly to do with the replicability crisis." Moreover, "It is important for readers not to think that Bayesian analysis merely amounts to using Bayes factors for hypothesis testing instead of using p values for hypothesis testing. In fact, the larger part of Bayesian analysis is a rich framework for estimating the magnitudes of parameters (such as effect size) and their uncertainties. Bayesian methods are also rich tools for meta-analysis and cumulative analysis. Therefore, Bayesian methods achieve all the goals of the New Statistics (Cumming, 2014) but without using p values and confidence intervals."
See the full article and comment at the link above.
Please comment on the article here: Doing Bayesian Data Analysis
