Benford's Law: Textbook Exercises and Multiple-choice Testbanks
Aaron D. Slepkov, Kevin B. Ironside, and David DiBattista

TL;DR
This paper explores how physics and chemistry textbook exercises follow Benford's Law and examines whether this pattern can be exploited to cheat on multiple-choice tests, finding limited practical vulnerability.
Contribution
It demonstrates textbook exercise answers conform to Benford's Law and assesses the potential for using this pattern to improve guessing strategies in tests.
Findings
Textbook answers follow Benford's Law
Testbank answers conform to Benford's Law
Testbank is secure against Benford-based guessing
Abstract
Benford's Law describes the finding that the distribution of leading (or leftmost) digits of innumerable datasets follows a well-defined logarithmic trend, rather than an intuitive uniformity. In practice this means that the most common leading digit is 1, with an expected frequency of 30.1%, and the least common is 9, with an expected frequency of 4.6%. The history and development of Benford's Law is inexorably linked to physics, yet there has been a dearth of physics-related Benford datasets reported in the literature. Currently, the most common application of Benford's Law is in detecting number invention and tampering such as found in accounting-, tax-, and voter-fraud. We demonstrate that answers to end-of-chapter exercises in physics and chemistry textbooks conform to Benford's Law. Subsequently, we investigate whether this fact can be used to gain advantage over random guessing…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBenford’s Law and Fraud Detection
