Forum on Benford’s law and statistical methods for the detection of frauds

Barabesi, Lucio; Cerioli, Andrea; Perrotta, Domenico

doi:10.1007/s10260-021-00588-0

Cited by 9 publications

(2 citation statements)

References 41 publications

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“…While our results demonstrate the promise of our approach, there are several limitations to consider. First, our method relies on the assumption that the reported aggregated data follow Benford's distribution, which may not always hold [52,53]. Second, our approach requires the development of tests for each statistical operator, making it hard to utilize a wide spectrum of fields and manuscripts that may use and report a large number of different statistical analysis methods.…”

Section: Discussionmentioning

confidence: 99%

Can We Mathematically Spot the Possible Manipulation of Results in Research Manuscripts Using Benford’s Law?

Lazebnik,

Gorlitsky

2023

Data

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The reproducibility of academic research has long been a persistent issue, contradicting one of the fundamental principles of science. Recently, there has been an increasing number of false claims found in academic manuscripts, casting doubt on the validity of reported results. In this paper, we utilize an adapted version of Benford’s law, a statistical phenomenon that describes the distribution of leading digits in naturally occurring datasets, to identify the potential manipulation of results in research manuscripts, solely using the aggregated data presented in those manuscripts rather than the commonly unavailable raw datasets. Our methodology applies the principles of Benford’s law to commonly employed analyses in academic manuscripts, thus reducing the need for the raw data itself. To validate our approach, we employed 100 open-source datasets and successfully predicted 79% of them accurately using our rules. Moreover, we tested the proposed method on known retracted manuscripts, showing that around half (48.6%) can be detected using the proposed method. Additionally, we analyzed 100 manuscripts published in the last two years across ten prominent economic journals, with 10 manuscripts randomly sampled from each journal. Our analysis predicted a 3% occurrence of results manipulation with a 96% confidence level. Our findings show that Benford’s law adapted for aggregated data, can be an initial tool for identifying data manipulation; however, it is not a silver bullet, requiring further investigation for each flagged manuscript due to the relatively low prediction accuracy.

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Section: Discussionmentioning

confidence: 99%

Can We Mathematically Spot the Possible Manipulation of Results in Research Manuscripts Using Benford’s Law?

Lazebnik,

Gorlitsky

2023

Data

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“…Benford's law is widely used in various fields because it detects anomalous data in a data set. If further explained, such data anomalies can help detect fraud [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Benford’s law analysis to evaluate the quality data of COVID-19 epidemiological surveillance in Indonesia

Sinaga,

Sudharma

2024

IJPHS

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Countries worldwide, including Indonesia, grappled with the unprecedented challenges brought about by the coronavirus disease (COVID-19) pandemic. Surveillance data vividly illustrates the profound effect of the COVID-19 pandemic in Indonesia. Both daily cases and deaths were raised, revealing the rapid transmission of the virus within communities. A quantitative study using a statistical approach was accomplished with secondary data to evaluate the quality of COVID-19 epidemiological surveillance data in Indonesia during the period between March 2020 to January 2021. The data was sourced from the World Health Organization (WHO) website using data reports on COVID-19 confirmed cases and deaths. A rapid tool called the first digit law or the fulfillment of Benford’s law was used to suggest good data quality for epidemiological surveillance. Data analysis used the Chi-squared test and the log-likelihood ratio test. Also, it displayed the difference in mean absolute deviation (MAD) to identify the proximity of the data and Benford’s law distribution. The results showed that both confirmed, and death case distributions were statistically non-conformity with Benford’s law distribution. In terms of quality data regarding the COVID-19 pandemic, the epidemiological surveillance system falls short of Benford’s law assumption. Benford's law has been acknowledged as an initial analysis that can expeditiously assess the performance of a surveillance system. The next phase of this study would be to conduct a complete evaluation suitably, especially in post-pandemic COVID-19.

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Wem man glauben kann: Aufgedeckte Gaunereien

Müller

Quatember

2022

Fakt Oder Fake? Wie Ihnen Statistik Bei Der Unterscheidung Helfen Kann

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Forum on Benford’s law and statistical methods for the detection of frauds

Cited by 9 publications

References 41 publications

Can We Mathematically Spot the Possible Manipulation of Results in Research Manuscripts Using Benford’s Law?

Can We Mathematically Spot the Possible Manipulation of Results in Research Manuscripts Using Benford’s Law?

Benford’s law analysis to evaluate the quality data of COVID-19 epidemiological surveillance in Indonesia

Wem man glauben kann: Aufgedeckte Gaunereien

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