Many perspectives on Deborah Mayo’s “Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars”

This is not new—these reviews appeared in slightly rawer form several months ago on the blog.

After that, I reorganized the material slightly and sent to Harvard Data Science Review (motto: “A Microscopic, Telescopic, and Kaleidoscopic View of Data Science”) but unfortunately reached a reviewer who (a) didn’t like Mayo’s book, and (b) felt that our article was unfocused. I can’t say much about (a), but (b) is fair enough.

Nonetheless it seemed to me that, unfocused or not, our article—this annotated collection of reactions to a controversial recent book on the philosophy of statistics (and how often can one say that???) could be of some interest within the statistical modeling, casual inference, and social science community.

So here I’m posting our article (by Andrew Gelman, Brian Haig, Christian Hennig, Art B. Owen, Robert Cousins, Stan Young, Christian Robert, Corey Yanofsky, E. J. Wagenmakers, Ron Kenett, and Daniel Lakeland). Enjoy.