When I start my Bayesian class, I like to mention some reasons why this is a relevant class. Specifically, what is wrong with frequentist inference and what can Bayes thinking add the statistician’s toolkit?

There is an article published in Science about 10 years ago titled “Bayes Offers a ‘New’ Way to Make Sense of Numbers” that you can find at

http://bayes.bgsu.edu/m6480/LECTURE%20NOTES/science.article.pdf

It does a good job selling Bayes to the public. Here are a couple of things from the article that I mentioned in my class.

1. Part of the motivation for considering Bayesian methods are the advances in computers and computational methods together with some limitations of frequentist methods.

2. Bayesian conclusions are easier to understand.

3. The FDA is currently encouraging more use of Bayesian methods for clinical trials. One area where Bayesian methods appear to have an advantage is sequential trials where one is collecting data in time and one wishes to stop the trial when one has sufficient evidence to make a decision.

4. P-values, one of the standard frequentist summaries, are frequently misinterpreted. In addition, there is a strong literature that suggests that p-values typically overstate the evidence against the null hypothesis.

5. One the popular computer tools is the Microsoft animated paperclip http://en.wikipedia.org/wiki/Office_Assistant that is driven by Bayesian methods. But it seems that people are generally annoyed with this help device and it is going away.