Modern Science and the Bayesian-Frequentist Controversy

February 14, 2011

(This article was first published on John Myles White » Statistics, and kindly contributed to R-bloggers)

The Bayesian-Frequentist debate reflects two different attitudes to the process of doing science, both quite legitimate. Bayesian statistics is well-suited to individual researchers, or a research group, trying to use all the information at its disposal to make the quickest possible progress. In pursuing progress, Bayesians tend to be aggressive and optimistic with their modeling assumptions. Frequentist statisticians are more cautious and defensive. One definition says that a frequentist is a Bayesian trying to do well, or at least not too badly, against any possible prior distribution. The frequentist aims for universally acceptable conclusions, ones that will stand up to adversarial scrutiny. The FDA for example doesn’t care about Pfizer’s prior opinion of how well it’s new drug will work, it wants objective proof. Pfizer, on the other hand may care very much about its own opinions in planning future drug development.1

To me, it’s amazing how similar the ambiguous regions of behavioral decision theory are to the major questions of theoretical statistics: people seem largely unable to systematically decide whether they want to be minimaxing (which seems very close to Efron’s vision of frequentist thought as stated here) or whether they want to be minimizing expected risk (which is closer to my own vision of Bayesian thinking). My own sense is that we learn as a global culture, over time, which error functions are least erroneous — and we do so largely by trial and error.

Most interesting to me is to consider individual differences in the error functions people effectively use: I suspect political preferences correlate with a propensity to focus on worst case thinking rather than average case thinking. Also, I’m fascinated by the way that a single person switches between worst case and average case thinking: I suspect there’s as much to be learned here as there was in understanding what drives risk seeking behavior and what drives risk average behavior.

HT: John D. Cook

  1. Bradley Efron : Modern Science and the Bayesian-Frequentist Controversy

To leave a comment for the author, please follow the link and comment on their blog: John Myles White » Statistics. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Tags: ,

Comments are closed.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training


CRC R books series

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)