Dr. Frank Benford, a physicist, demonstrated in the 1930's that the frequency of the first digits in any table of unmanipulated data follow a predictable pattern, which now bears his name. He calculated the expected rate of occurrence for the first digit with a logarithmic distribution formula. When data is manipulated, as it is in a fraud, the frequency of appearance of the initial digits usually differs from Benford's predicted frequency, which makes his law a potentially powerful tool for fraud detection. Using Benford's formula and commonly available spreadsheet software such as Microsoft Excel or Lotus 123, a fraud examiner or auditor can calculate and graph the anticipated frequency of occurrence for any population. In his new book, "Digital Analysis Using Benford's Law," Mark J. Nigrini suggests the use of Mean Absolute Deviation (MAD) as the best measure of closeness of fit for DFA. Calculating MAD is not difficult, particularly when using spreadsheet software. The result provides a number that helps to tell a story about the data examined. This article describes the step-by-step procedure for doing this. 5 exhibits