Volume 1 - Issue 4, November - December 2025

Election integrity is essential for democratic governance and demands objective, data-driven methods to assess the credibility of electoral outcomes. This study applies Benford’s Law, a statistical tool used in forensic analysis, to examine the vote count distribution in the Delhi Assembly Elections. The Chi-square goodness-of-fit test was used to compare observed digit frequencies with the expected Benford distribution. The results reveal significant deviations (p-value < 0.05), suggesting possible irregularities. However, statistical deviations alone do not confirm electoral fraud; such deviations may arise from campaign strategies, demographic trends, regional political behavior, or structural limitations of Benford’s Law itself. The study also considers broader political and technical criticisms, including concerns raised by political parties and doubts about the reliability of Electronic Voting Machines (EVMs). Given the global use of Benford’s Law in election forensics, its application in the Indian context provides an evidence-based method to address public skepticism. This research highlights the value of statistical tools in strengthening electoral transparency and promoting data literacy in democratic systems.