Benford's Law is a statistical principle that predicts the frequency distribution of leading digits in naturally occurring datasets. It states that in many real-world datasets, the first digit is more likely to be small, with "1" appearing about 30% of the time, while higher digits like "9" occur less frequently (around 5%). This logarithmic distribution applies to diverse data sets, such as financial transactions, population numbers, or scientific measurements. Benford's Law is often used in fraud detection, as manipulated data tends to deviate from this expected pattern. It works best with datasets spanning multiple orders of magnitude and is scale-invariant.
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