Fiddling the figures: Benford reveals all

Well, some of it, anyway. There’s been quite a lot of coverage in on the web recently about Benford’s law and the Greek debt crisis.

As I’m sure you remember, Benford’s law says that in lists of numbers from many real life sources of data, the leading digit isn’t uniformly distributed. In fact, around 30% of leading digits are 1, while fewer than 5% are 9. The phenomenon has been known for some time, and is often used to detect possible fraud – if people are cooking the books, they don’t usually get the distributions right.

It’s been in the news because it turns out that the macroeconomic data reported by Greece shows the greatest deviation from Benford’s law among all euro states (hat tip Marginal Revolution).

There was also a possible result that the numbers in published accounts in the financial industry deviated more from Benford’s law now than they used to. But it now appears that the analysis may be faulty.

How else can Benford’s law be used? What about testing the results of stochastic modelling, for example? If the phenomena we are trying to model are ones for which Benford’s law works, then the results of the model should comply too.

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