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Empirical Information on the Small Size Effect Bias Relative to the False Positive Rejection Error for Benford Test-Screening

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  • Yan Bao
  • Chuo-Hsuan Lee
  • Frank Heilig
  • Edward J. Lusk

Abstract

Due to the theoretical work of Hill Benford digital profile testing is now a staple in screening data for forensic investigations and audit examinations. Prior empirical literature indicates that Benford testing when applied to a large Benford Conforming Dataset often produces a bias called the FPE Screening Signal [FPESS] that misleads investigators into believing that the dataset is Non-Conforming in nature. Interestingly, the same FPESS can also be observed when investigators partition large datasets into smaller datasets to address a variety of auditing questions. In this study, we fill the empirical gap in the literature by investigating the sensitivity of the FPESS to partitioned datasets. We randomly selected 16 balance-sheet datasets from: China Stock Market Financial Statements Database?, that tested to be Benford Conforming noted as RBCD. We then explore how partitioning these datasets affects the FPESS by repeated randomly sampling: first 10% of the RBCD and then selecting 250 observations from the RBCD. This created two partitioned groups of 160 datasets each. The Statistical profile observed was: For the RBCD there were no indications of Non-Conformity; for the 10%-Sample there were no overall indications that Extended Procedures would be warranted; and for the 250-Sample there were a number of indications that the dataset was Non-Conforming. This demonstrated clearly that small datasets are indeed likely to create the FPESS. We offer a discussion of these results with implications for audits in the Big-Data context where the audit In-charge would find it necessary to partition the datasets of the client.

Suggested Citation

  • Yan Bao & Chuo-Hsuan Lee & Frank Heilig & Edward J. Lusk, 2018. "Empirical Information on the Small Size Effect Bias Relative to the False Positive Rejection Error for Benford Test-Screening," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(2), pages 1-9, February.
  • Handle: RePEc:ibn:ijefaa:v:10:y:2018:i:2:p:1-9
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    References listed on IDEAS

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    1. Tam Cho, Wendy K. & Gaines, Brian J., 2007. "Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance," The American Statistician, American Statistical Association, vol. 61, pages 218-223, August.
    2. T. Mir, 2016. "The leading digit distribution of the worldwide illicit financial flows," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 271-281, January.
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    Cited by:

    1. Yan Bao & Frank Heilig & Chuo-Hsuan Lee & Edward Lusk, 2018. "Full Range Testing of the Small Size Effect Bias for Benford Screening: A Note," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(6), pages 47-52, June.

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    More about this item

    Keywords

    extended procedures; audit risk; partitioning large datasets;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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