We and others have noted a discouraging tendency in the Freakonomics body of work to present speculative or even erroneous claims with an air of certainty...

In our analysis of the Freakonomics approach, we encountered a range of avoidable mistakes, from back-of-the-envelope analyses gone wrong to unexamined assumptions to an uncritical reliance on the work of Levitt’s friends and colleagues. This turns accessibility on its head: Readers must work to discern which conclusions are fully quantitative, which are somewhat data driven and which are purely speculative.

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Predicting terrorists: In SuperFreakonomics, Levitt and Dubner introduce a British man, pseudonym Ian Horsley, who created an algorithm that used people’s banking activities to sniff out suspected terrorists. They rely on a napkin-simple computation to show the algorithm’s “great predictive power”:

The straw man they employ—a hypothetical algorithm boasting 99-percent accuracy—would indeed, if it exists, wrongfully accuse half a million people out of the 50 million adults in the United Kingdom. So the conventional wisdom that 99-percent accuracy is sufficient for terrorist prediction is folly, as has been pointed out by others such as security expert Bruce Schneier.

But in the course of this absorbing narrative, readers may well miss the spot where Horsley’s algorithm also strikes out. The casual computation keeps under wraps the rate at which it fails at catching terrorists: With 500 terrorists at large (the authors’ supposition), the “great” algorithm finds only five of them. Levitt and Dubner acknowledge that “five out of 30 isn’t perfect,” but had they noticed the magnitude of false negatives generated by Horsley’s secret recipe, and the grave consequences of such errors, they might have stopped short of hailing his story. The maligned straw-man algorithm, by contrast, would have correctly identified 495 of 500 terrorists.
Full article: http://www.americanscientist.org/issues/id.14344,y.0,no.,content.true,page.3,css.print/issue.aspx