.
In my new paper, “Severe Testing: Error Statistics versus Bayes Factor Tests”, now out online at the The British Journal for the Philosophy of Science, I “propose that commonly used Bayes factor tests be supplemented with a post-data severity concept in the frequentist error statistical sense”. But how? I invite your thoughts on this and any aspect of the paper.* (You can read it here.)
I’m pasting down the abstract and the introduction. Continue reading
I. A principled disagreement
The other day I was in a practice (zoom) for a panel I’m in on how different approaches and philosophies (Frequentist, Bayesian, machine learning) might explain “why we disagree” when interpreting clinical trial data. The focus is radiation oncology.[1] An important point of disagreement between frequentist (error statisticians) and Bayesians concerns whether and if so, how, to modify inferences in the face of a variety of selection effects, multiple testing, and stopping for interim analysis. Such multiplicities directly alter the capabilities of methods to avoid erroneously interpreting data, so the frequentist error probabilities are altered. By contrast, if an account conditions on the observed data, error probabilities drop out, and we get principles such as the stopping rule principle. My presentation included a quote from Bayarri and J. Berger (2004): Continue reading
.
My new paper, “P Values on Trial: Selective Reporting of (Best Practice Guides Against) Selective Reporting” is out in Harvard Data Science Review (HDSR). HDSR describes itself as a A Microscopic, Telescopic, and Kaleidoscopic View of Data Science. The editor-in-chief is Xiao-li Meng, a statistician at Harvard. He writes a short blurb on each article in his opening editorial of the issue. Continue reading
Experts convene to explore new philosophy of statistics field
Error Statistics Philosophy 