Accuracy of comparison decisions by forensic firearms examiners.

Keith L Monson, Erich D Smith, Eugene M Peters
Author Information
  1. Keith L Monson: Federal Bureau of Investigation Laboratory, Quantico, Virginia, USA. ORCID
  2. Erich D Smith: Federal Bureau of Investigation Laboratory, Quantico, Virginia, USA.
  3. Eugene M Peters: Federal Bureau of Investigation Laboratory, Quantico, Virginia, USA.

Abstract

This black box study assessed the performance of forensic firearms examiners in the United States. It involved three different types of firearms and 173 volunteers who performed a total of 8640 comparisons of both bullets and cartridge cases. The overall false-positive error rate was estimated as 0.656% and 0.933% for bullets and cartridge cases, respectively, while the rate of false negatives was estimated as 2.87% and 1.87% for bullets and cartridge cases, respectively. The majority of errors were made by a limited number of examiners. Because chi-square tests of independence strongly suggest that error probabilities are not the same for each examiner, these are maximum-likelihood estimates based on the beta-binomial probability model and do not depend on an assumption of equal examiner-specific error rates. Corresponding 95% confidence intervals are (0.305%, 1.42%) and (0.548%, 1.57%) for false positives for bullets and cartridge cases, respectively, and (1.89%, 4.26%) and (1.16%, 2.99%) for false negatives for bullets and cartridge cases, respectively. The results of this study are consistent with prior studies, despite its comprehensive design and challenging specimens.

Keywords

References

  1. Sci Justice. 2015 Dec;55(6):514-9 [PMID: 26654088]
  2. J Forensic Sci. 2023 Jan;68(1):86-100 [PMID: 36183147]
  3. J Forensic Sci. 2022 May;67(3):936-954 [PMID: 35322424]
  4. Forensic Sci Int Synerg. 2020 Sep 06;2:333-338 [PMID: 33385131]
  5. J Forensic Sci. 2021 Mar;66(2):547-556 [PMID: 33104244]
  6. J Forensic Sci. 2021 Sep;66(5):1704-1720 [PMID: 34057735]
  7. J Forensic Sci. 2019 May;64(3):728-740 [PMID: 30444940]
  8. Forensic Sci Int. 2017 Feb;271:98-106 [PMID: 28073053]
  9. J Forensic Sci. 2007 May;52(3):586-94 [PMID: 17456086]
  10. Sci Justice. 2018 Jul;58(4):258-263 [PMID: 29895457]
  11. Sci Justice. 2016 Mar;56(2):129-42 [PMID: 26976472]
  12. J Forensic Sci. 2019 Jan;64(1):10-15 [PMID: 29975992]
  13. Forensic Sci Int. 2012 Jun 10;219(1-3):183-98 [PMID: 22269131]
  14. Proc Natl Acad Sci U S A. 2011 May 10;108(19):7733-8 [PMID: 21518906]
  15. Front Psychol. 2019 Mar 19;10:520 [PMID: 30941075]
  16. Forensic Sci Int. 2021 Jan;318:110457 [PMID: 33239260]
  17. J Forensic Sci. 2019 Mar;64(2):551-557 [PMID: 30261099]
  18. J Forensic Sci. 2016 Jul;61(4):939-46 [PMID: 27135174]
  19. Forensic Sci Int Synerg. 2020 Mar 04;2:389-403 [PMID: 33385138]
  20. Sci Justice. 2002 Oct-Dec;42(4):197-203 [PMID: 12632935]
  21. J Forensic Sci. 2022 Mar;67(2):516-523 [PMID: 34806779]
  22. Forensic Sci Int. 2020 Feb;307:110112 [PMID: 31881373]
  23. J Forensic Sci. 2018 Mar;63(2):440-448 [PMID: 28691746]
  24. Forensic Sci Int. 2020 Nov;316:110542 [PMID: 33147525]
  25. Sci Justice. 2008 Dec;48(4):178-81 [PMID: 19192679]
  26. J Forensic Sci. 2019 Sep;64(5):1324-1334 [PMID: 30859567]
  27. J Forensic Sci. 2022 Mar;67(2):834-836 [PMID: 34951706]
  28. Cogn Sci. 2012 Aug;36(6):1019-50 [PMID: 22578040]
  29. J Forensic Sci. 2018 Jul;63(4):1069-1084 [PMID: 29044577]
  30. Forensic Sci Int Synerg. 2022 Feb 19;4:100221 [PMID: 35243285]
  31. J Forensic Sci. 2017 May;62(3):619-625 [PMID: 28449257]
  32. J Forensic Sci. 2021 Mar;66(2):557-570 [PMID: 33104255]
  33. Forensic Sci Int Synerg. 2021 Apr 17;3:100147 [PMID: 33981984]
  34. J Forensic Sci. 2014 May;59(3):637-47 [PMID: 24502645]

MeSH Term

Humans
Firearms
Forensic Medicine
Models, Statistical
Likelihood Functions

Word Cloud

Created with Highcharts 10.0.0bulletscartridgecases1firearmserror0respectivelyexaminersratefalseblackboxstudyforensicestimatednegatives287%designassessedperformanceUnitedStatesinvolvedthreedifferenttypes173volunteersperformedtotal8640comparisonsoverallfalse-positive656%933%majorityerrorsmadelimitednumberchi-squaretestsindependencestronglysuggestprobabilitiesexaminermaximum-likelihoodestimatesbasedbeta-binomialprobabilitymodeldependassumptionequalexaminer-specificratesCorresponding95%confidenceintervals305%42%548%57%positives89%426%16%99%resultsconsistentpriorstudiesdespitecomprehensivechallengingspecimensAccuracycomparisondecisionsaccuracyconsecutivemanufacturedecisionanalysistoolmarkidentificationfoundationalvalidityopensetreliabilitysubclass

Similar Articles

Cited By