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Slepkov, AD, Ironside, KB and DiBattista, D (2015). Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks. PLoS ONE 10(2): e0117972.

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Amanda Bowman, A (2016). Contributions to the Testing of Benford's Law. Masters Thesis, McMaster University, Hamilton, Canada. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Burgos, A and Santos, A (2021). The Newcomb–Benford law: Scale invariance and a simple Markov process based on it (Previous title: The Newcomb–Benford law: Do physicists use more frequently the key 1 than the key 9?). Preprint arXiv:2101.12068 [physics.pop-ph]; last accessed August 8, 2022; Published Am. J. Phys. 89, pp. 851-861. View Complete Reference Online information Works that this work references Works that reference this work
Cerqueti, R and Maggi, M (2021). Data validity and statistical conformity with Benford’s Law. Chaos, Solitons & Fractals 144, p. 110740 . DOI:10.1016/j.chaos.2021.110740. View Complete Reference Online information Works that this work references Works that reference this work
da Silva, AJ, Floquet, S, Santos, DOC and Lima, RF (2020). On the validation of the Newcomb-Benford Law and the Weibull distribution in neuromuscular transmission. Physica A 553, 1 September 2020, 124606. DOI:10.1016/j.physa.2020.124606. View Complete Reference Online information Works that this work references Works that reference this work
Farhadi, N and Lahooti, H (2021). Are COVID-19 Data Reliable? A Quantitative Analysis of Pandemic Data from 182 Countries. COVID 1, pp. 137–152. DOI:10.3390/covid1010013. View Complete Reference Online information Works that this work references Works that reference this work
Horton, J, Kumar, DK and Wood, A (2020). Detecting academic fraud using Benford law: The case of Professor James Hunton. Research Policy 49(8), 104084 . DOI:10.1016/j.respol.2020.104084. View Complete Reference Online information Works that this work references Works that reference this work
Hull, B, Long, A and Hughes, IG (2022). Using residual heat maps to visualise Benford's multi-digit law. European Journal of Physics 43, 015803. DOI:10.1088/1361-6404/ac3671. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Kennedy, AP and Yam, SCP (2020). On the authenticity of COVID-19 case figures. PLoS ONE 15(12): e0243123. DOI:10.1371/journal.pone.0243123. View Complete Reference Online information Works that this work references Works that reference this work
Kossovsky, AE (2021). On the Mistaken Use of the Chi-Square Test in Benford’s Law. Stats 4(2), pp. 419–453. DOI:10.3390/stats4020027. View Complete Reference Online information Works that this work references Works that reference this work
Lanham, SW (2019). Analyzing Big Data with Benford’s Law: A Lesson for the Classroom. American Journal of Business Education 12(2), pp. 33-42. DOI:10.19030/ajbe.v12i2.10285. View Complete Reference Online information Works that this work references Works that reference this work
McCarville, D (2021). A data transformation process for using Benford’s Law with bounded data. Preprint [version 1; peer review: awaiting peer review], Emerald Open Research 3(29). DOI:10.35241/emeraldopenres.14374.1. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Nigrini, MJ (2016). The Implications of the Similarity between Fraud Numbers and the Numbers in Financial Accounting Textbooks and Test Banks. Journal of Forensic Accounting Research, Vol. 1, No. 1, pp. A1-A26. DOI:10.2308/jfar-51465. View Complete Reference Online information Works that this work references Works that reference this work
Nigrini, MJ (2017). Audit Sampling Using Benford's Law: A Review of the Literature With Some New Perspectives. Journal of Emerging Technologies in Accounting Vol. 14, No. 2, pp. 29–46. DOI:10.2308/jeta-51783. View Complete Reference Online information Works that this work references Works that reference this work
Wang, D, Chen, F, Mao, J, Liu, N and Rong, F (2022). Are the official national data credible? Empirical evidence from statistics quality evaluation of China's coal and its downstream industries . Energy Economics, p. 106310. DOI:10.1016/j.eneco.2022.106310. View Complete Reference Online information Works that this work references No Bibliography works reference this work