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Glen, S (2020). Fraudulent Covid-19 Data and Benford's Law. Blog posted on December 31; last accessed February 15, 2021.

This work cites the following items of the Benford Online Bibliography:

Deckert, J, Myagkov, M and Ordeshook, PC (2011). Benford's Law and the Detection of Election Fraud. Political Analysis 19(3), pp. 245-268. DOI:10.1093/pan/mpr014. View Complete Reference Online information Works that this work references Works that reference this work
Diekmann, A (2007). Not the First Digit! Using Benford's Law to Detect Fraudulent Scientific Data. Journal of Applied Statistics 34(3), pp. 321-329. ISSN/ISBN:0266-4763. DOI:10.1080/02664760601004940. View Complete Reference Online information Works that this work references Works that reference this work
Frunza, M-C (2016). Solving Modern Crime in Financial Markets: Analytics and Case Studies. Academic Press, New York, (Chapter 2K) pp. 233-245. DOI:10.1016/B978-0-12-804494-0.00017-6. View Complete Reference Online information Works that this work references Works that 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
Klimek, P, Jiménez, R, Hidalgo, M, Hinteregger, A and Thurner, S (2018). Forensic analysis of Turkish elections in 2017–2018. PLoS ONE 13(10), pp. e0204975. DOI:10.1371/journal.pone.0204975. View Complete Reference Online information Works that this work references Works that reference this work