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Sample statistics as convincing evidence: A tax fraud case

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Abstract

This report deals with the analysis of data used by tax officers to support their claim of tax fraud at a pizzeria. The possibilities of embezzlement under study are overreporting of take-away sales and underreporting of cash payments. Several modelling approaches are explored, ranging from simple well-known methods to presumably more precise tools. More specifically, we contrast common methods based on normal assumptions and models based on Gamma-assumptions. For the latter, both maximum likelihood and Bayesian approaches are covered. Several criteria for the choice of method in practice are discussed, among them, how easy the method is to understand, justify and communicate to the parties. Some dilemmas present itself: the choice of statistical method, its role in building the evidence, the choice of risk factor, the application of legal principles like “clear and convincing evidence” and “beyond reasonable doubt”. The insights gained may be useful for both tax officers and defenders of the taxpayer, as well as for expert witnesses.

Suggested Citation

  • Lillestøl, Jostein, 2018. "Sample statistics as convincing evidence: A tax fraud case," Discussion Papers 2018/12, Norwegian School of Economics, Department of Business and Management Science.
  • Handle: RePEc:hhs:nhhfms:2018_012
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    1. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    2. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    3. Norman Fenton, 2011. "Improve statistics in court," Nature, Nature, vol. 479(7371), pages 36-37, November.
    4. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
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    More about this item

    Keywords

    Gamma-Beta analysis; Bayesian Gamma-analysis; Risk analysis;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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