Index of Post-Publication Peer-Review Comments on The Vindication of Magnitude-Based Inference
This page summarizes the links to review comments submitted by readers of The Vindication of Magnitude-Based Inference. See that article for a description of the post-publication peer-review process, a link there or here for a template in which to write your comments, and the link there or here to email your comments to the authors/editors.
Magnitude-based inference is a special case of “calibrated Bayes” inference that provides a good basis for inference in many settings. Jun 3
MBI should be placed on a more formal footing by focusing on either Frequentist error control or the quantification of Bayesian posterior probabilities. Jun 3
In our response to Little and Lakens, we highlight the need for MBI to be promoted and accepted as a valid form of Bayesian inference, in which probabilistic statements about the true magnitude of an effect are not modified by any prior belief or information about the effect. Jun 3
Recent criticisms about error rates in MBI are based on flawed logic, false assertions, and attempts to apply error-type definitions that do not apply to MBI. MBI is a rigorous and valuable statistical tool. Jun 17
MBI is irreplaceable for sport scientists. It is worth fighting for. #supportMBI Jul 2
We advise researchers to describe their inferences about magnitudes as the legitimate reference Bayes with a dispersed uniform prior when submitting manuscripts to any journal banning magnitude-based inference. We also address the latest attack on MBI. Jul 15
In this slideshow I explain the recent attack on MBI by showing how MBI works, how p values fail, how MBI is Bayesian, how errors can occur in MBI, and how the error rates are low and acceptable. The slideshow is also available as two YouTube videos of a chat between Fabio Serpiello and me: MBI and the recent attack on MBI and Errors and error rates with MBI. Aug 21. Updated final slide Nov 26.
Published June-November 2018.