I finally got around to reading "When Genius Failed", Roger Lowenstein's account of the spectacular collapse of LTCM, the hedge fund fronted by Scholes and Merton, Nobel laureates both.
It is a sobering read for anyone in the business of statistical prediction and modeling for sure.
What also caught my eye, and caused dismay, is how Lowenstein got basic statistical principles wrong in the book. He used the bully pulpit to sound the usual alarm against the normality assumption and for fat tails. He began by confusing LLN and CLT (central limit theorem):
Statisticians have long been aware of the "law of large numbers". Roughly speaking, if you have enough samples of a random event, they will tend to distribute in the familiar bell curve ...
In the same breadth, he then equated two different probability distributions:
This is called the normal distribution, or in mathematical terms, the lognormal distribution.
Doesn't this say something about the state of statistical literacy?
PS. Here is a link to Dunbar's "Inventing Money" (thanks Marc). It apparently came out before Lowenstein but didn't get as much press.