METRICS IN SMALL-SIZED QURAN DATASET FOR BENFORD’S LAW

Benford’s law is widely applied in testing anomalies in various dataset, including accounting fraud detection and population numbers. It is a statistical regularity, which is said that it works better with larger datasets that span large orders of magnitude distributed in a non-uniform way. In this study, we examine the potential metrics in small-sized Quran dataset that are applicable for the Benford’s law. Against our expectations, we find that the Quran dataset conforms to the Benford’s law. We provide evidence that metrics such as total paragraph per chapter and total verse per chapter conform to Benford’s distribution. However, total verse is closer to Benford’s law prediction compared to total paragraph.