Comment on Gelman’s “Induction and Deduction in Bayesian Data Analysis” (RMM)
Dr. Christian Hennig (Senior Lecturer, Department of Statistical Science, University College London)
I have read quite a bit of what Andrew Gelman has written in recent years, including some of his blog. One thing that I find particularly refreshing and important about his approach is that he contrasts the Bayesian and frequentist philosophical conceptions honestly with what happens in the practice of data analysis, which often cannot (or does better not to) proceed according to an inflexible dogmatic book of rules.
I also like the emphasis on the fact that all models are wrong. I personally believe that a good philosophy of statistics should consistently take into account that models are rather tools for thinking than able to “match” reality, and in the vast majority of cases we know clearly that they are wrong (all continuous models are wrong because all observed data are discrete, for a start).
There is, however, one issue on which I find his approach unsatisfactory (or at least not well enough explained), and on which both frequentism and subjective Bayesianism seem superior to me.