Citation

Murdock JE, Petraco NDK, Thornton JI, Neel MT, Weller TJ, Thompson RM, Hamby JE, Collins ER. J. Forensic Sci. 2017; 62(3): 619-625.

Affiliation

Contra Costa County Office of the Sheriff, Forensic Services Division, Martinez, CA.

Copyright

(Copyright © 2017, American Society for Testing and Materials, Publisher John Wiley and Sons)

DOI

10.1111/1556-4029.13386

PMID

28449257

Abstract

The field of firearms and toolmark analysis has encountered deep scrutiny of late, stemming from a handful of voices, primarily in the law and statistical communities. While strong scrutiny is a healthy and necessary part of any scientific endeavor, much of the current criticism leveled at firearm and toolmark analysis is, at best, misinformed and, at worst, punditry. One of the most persistent criticisms stems from the view that as the field lacks quantified random match probability data (or at least a firm statistical model) with which to calculate the probability of a false match, all expert testimony concerning firearm and toolmark identification or source attribution is unreliable and should be ruled inadmissible. However, this critique does not stem from the hard work of actually obtaining data and performing the scientific research required to support or reject current findings in the literature. Although there are sound reasons (described herein) why there is currently no unifying probabilistic model for the comparison of striated and impressed toolmarks as there is in the field of forensic DNA profiling, much statistical research has been, and continues to be, done to aid the criminal justice system. This research has thus far shown that error rate estimates for the field are very low, especially when compared to other forms of judicial error. The first purpose of this paper is to point out the logical fallacies in the arguments of a small group of pundits, who advocate a particular viewpoint but cloak it as fact and research. The second purpose is to give a balanced review of the literature regarding random match probability models and statistical applications that have been carried out in forensic firearm and toolmark analysis.

© 2017 American Academy of Forensic Sciences.

Language: en

Keywords

Daubert; coincidental match probability; correspondence probabilities; empirical error rate; error rate; false match error; firearm and toolmark identification; forensic science; likelihood ratio; probability; random match probabilities; random match probability; research; statistics; uncertainty