Affiliations: [a] Department of Political Science, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil | [b] Postgraduate Program in Medical Sciences, Universidade Federal de Alagoas, Maceió, Alagoas, Brazil
Correspondence:
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Corresponding author: Lucas Silva, Department of Medicine, Universidade Estadual de Ciências da Saúde do Estado de Alagoas, Maceió, Alagoas, Brazil. E-mail: [email protected].
Abstract: In this paper, we critically reevaluate Koch and Okamura’s (2020) conclusions on the conformity of Chinese COVID-19 data with Benford’s Law. Building on Figueiredo et al. (2022), we adopt a framework that combines multiple tests, including Chi-square, Kolmogorov-Smirnov, Euclidean Distance, Mean Absolute Deviation, Distortion Factor, and Mantissa Distribution. The primary rationale behind employing multiple tests is to enhance the robustness of our inference. The main finding of the study indicates that COVID-19 infections in China do not adhere to the distribution expected under Benford’s Law, nor does it align with the figures observed in the U.S. and Italy. The usefulness of deviations from Benford’s Law in detecting misreported or fraudulent data remains controversial. However, addressing this question requires a more careful statistical analysis than what is presented in the Koch and Okamura (2020) paper. By employing a combination of several tests using fully transparent procedures, we establish a more reliable approach to evaluating conformity to the Newcomb-Benford Law in applied research.
Keywords: Benford’s Law, COVID-19, conformity tests, first digit analysis, China
