Exact Bayesian Inference for Assessing the Accuracy of Polygraph Testing

Research Article

Abstract

The polygraph test is often used in law enforcement as an investigative tool, and in the courtroom where it is used to bolster support for the innocence of a defendant. The amount of available data to evaluate polygraph accuracy that is taken under realistic conditions is severely limited. With a fully Bayesian approach we analyze the largest such data set that exists. We derive exact results for the posterior distribution of the negative and positive predictive values, which can be evaluated with a computer algebra system. We show that the uncertainties, even given the largest and most realistic data set currently available, are great, casting doubt on the use of polygraph testing in criminal trials.

Keywords

Positive predictive value Negative predictive value Sensitivity Specificity Dirichlet distribution 

Notes

Acknowledgements

The author would like to thank the editor and the reviewer for their careful review of the manuscript. Their comments have certainly led to a better paper.

References

  1. Abramowitz M, Stegun IA (1964) Handbook of mathematical functions: with formulas, graphs, and mathematical tables, vol 55. Courier Corporation, North ChelmsfordMATHGoogle Scholar
  2. Ben-Shakhar Gershon, Dolev Karmela (1996) Psychophysiological detection through the guilty knowledge technique: effect of mental countermeasures. J Appl Psychol 81(3):273CrossRefGoogle Scholar
  3. Brett A, Phillips M, Beary J (1986) Predictive power of the polygraph: can the “lie detector” really detect liars? The Lancet 327(8480):544–547CrossRefGoogle Scholar
  4. Kelly Jack (2004) The truth about the lie detector. Am Herit Invent Technol 19(3):14–21Google Scholar
  5. Krapohl DJ (1996) A taxonomy of polygraph countermeasures. Polygraph 25(1):35–56Google Scholar
  6. Patrick Christopher J, Iacono Wiliam G (1991) Validity of the control question polygraph test: The problem of sampling bias. J Appl Psychol 76(2):229CrossRefGoogle Scholar
  7. Wolfram Research Inc., (2010) Mathematica, Version 11.0, Champaign, ILGoogle Scholar
  8. Zelicoff Alan, Rigdon Steven E (2017) Bayesian inference for interpretation of polygraph results in the courtroom. Law Probab Risk 16(2–3):91–109Google Scholar

Copyright information

© The Indian Society for Probability and Statistics (ISPS) 2018

Authors and Affiliations

  1. 1.Saint Louis UniversitySt. LouisUSA

Personalised recommendations