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Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks

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 th...

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Detalles Bibliográficos
Autores principales: Slepkov, Aaron D., Ironside, Kevin B., DiBattista, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331362/
https://www.ncbi.nlm.nih.gov/pubmed/25689468
http://dx.doi.org/10.1371/journal.pone.0117972
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author Slepkov, Aaron D.
Ironside, Kevin B.
DiBattista, David
author_facet Slepkov, Aaron D.
Ironside, Kevin B.
DiBattista, David
author_sort Slepkov, Aaron D.
collection PubMed
description 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%. 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 in multiple-choice tests, and find that while testbank answers in introductory physics closely conform to Benford’s Law, the testbank is nonetheless secure against such a Benford’s attack for banal reasons.
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spelling pubmed-43313622015-02-24 Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks Slepkov, Aaron D. Ironside, Kevin B. DiBattista, David PLoS One Research Article 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%. 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 in multiple-choice tests, and find that while testbank answers in introductory physics closely conform to Benford’s Law, the testbank is nonetheless secure against such a Benford’s attack for banal reasons. Public Library of Science 2015-02-17 /pmc/articles/PMC4331362/ /pubmed/25689468 http://dx.doi.org/10.1371/journal.pone.0117972 Text en © 2015 Slepkov et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Slepkov, Aaron D.
Ironside, Kevin B.
DiBattista, David
Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks
title Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks
title_full Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks
title_fullStr Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks
title_full_unstemmed Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks
title_short Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks
title_sort benford’s law: textbook exercises and multiple-choice testbanks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331362/
https://www.ncbi.nlm.nih.gov/pubmed/25689468
http://dx.doi.org/10.1371/journal.pone.0117972
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