## Statistical literacy

##### Aug 01, 2006

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.

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I have to echo Michael Crichton on this: if Lowenstein isn't credible when he talks about statistics, how can we believe what he says about anything else?

Lowenstein's book struck me as a fictionalized version of events: more of a good story than an acurate history.

By far the best account of events I've read is Nicholas Dunbar's Inventing Money. He clearly understands both the math and the finance involved.

Eww, Crichton. If he isn't credible while convincing the US president that global warming doesn't exist, how can we believe what he says about anything else?

In re LTCM, I missed the books, but I know the story. Sobering, 'tis true, but a good lesson always to recall your assumptions, and to remember that leverage really does work wonders.

I think you meant to write "in the same breath", not "breadth".

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