In the wake of continuing turmoil over the disputed Iranian election, enough information is available to do some validation of the results. There are several types of information – examples can be seen here and here.

This type of data can be used to look for fraud. Crude fraud can be found by applying Benford’s law, which states that for many categories of data, including vote counts, the first digits of lists of observations are not uniformly distributed (as one would naively expect) but instead skewed toward low values. There are more sophisticated tests as well. Prof. Walter Mebane at the University of Michigan is knowledgeable about such analysis and is applying the methods to the data from the recent Iranian election. In addition to intrinsic peculiarities such as Benford’s law, he is also using 2005 election data as a baseline to help discover unexpected anomalies.

He currently says “”I think the results give moderately strong support for a diagnosis that the 2009 election was affected by significant fraud.” I haven’t had time to go over his analysis, but here it is for interested readers, along with a ZIP file of source code and data. Note that he is still updating his analysis, so regard this as an interim report.

Later on I’ll take a critical look at this and other approaches to detecting fraud, with the idea of arriving at a synthesis – and possible conclusions.

Thanks to David Shor and Andrew Gelman for making this connection.