Stat Geek Post of the (Week?)

From Phil Price at "Statistical Modeling..."

A recent NY Times article/blog post discusses a classic Bayes' Theorem application -- probability that the patient has cancer, given a "positive" mammogram -- and purports to give a solution that is easy for students to understand because it doesn't require Bayes' Theorem, which is of course complicated and confusing.

The comment Phil made is priceless. Here is an excerpt:

I'm both perplexed and frustrated by your characterization of the "common sense" cancer calculation, because (except for the rounding) you have indeed applied Bayes' Theorem, whether you know it or not. ... Your calculation is EXACTLY as "labyrinthine" as Bayes' Theorem, because it is EXACTLY THE SAME as Bayes' theorem. Rather than telling your students that you have a better way of doing the calculation that avoids the complexities of Bayes' Theorem --- a claim that isn't true --- you should tell them that you can explain why Bayes' Theorem makes sense.

These journalists are one and the same who excoriate bloggers who provide information for free because it cannot possibly be "as good" as information that goes through the lens of journalistic editing. Riiiight. Or maybe experts are sick of being misquoted and taken out of contexts by journalists and figure the only way to do something right is to do it yourself.