Episode 235 - Todd Weller Interview

03/12/21 • 97 min

Glenn Langenburg and Eric Ray are joined by Todd Weller to discuss the firearms/toolmarks discipline. Todd was involved in the NY firearms case from Episode 227 and shares his experiences there. The guys then talk through accuracy studies for firearms examiners and similarities and differences between firearms and fingerprints. Lots to cover in an extra-long episode.

Previous Episode

Episode 234 - Palm Print Black Box Study

In this episode, Eric and Glenn are joined by Dr. Heidi Eldridge and Prof, Christophe Champod, the authors of "Testing the accuracy and reliability of palmar friction ridge comparisons - A black box study". The guests describe the design, the aims, and the results of the study. We get into discussions about the meaning and usefulness of error rate statistics and confidence intervals from performance studies such as this. Link to the Article (open source): http://dx.doi.org/10.1016/j.forsciint.2020.110457 Link to study results in shinyapp: https://cchampod.shinyapps.io/Results_BBStudy/ Link to confidence interval tools in shinyapp: https://cchampod.shinyapps.io/app_CI/ Link to error rate primer prepared by Champod, Eldridge, Lambert: zenodo.org/record/3734560#.YB8Ki5NKhbt

Next Episode

Episode 236 - Mindset Paper (Palms Part Deux!)

In this episode, the guys return for Round 2 with Dr. Heidi Eldridge and Prof. Christophe Champod. This time, they discuss the second paper arising from the palm print black box study (discussed in DLP Episode #234). The focus in this episode is “Mind-set – How bias leads to errors in friction ridge comparisons” by Eldridge, de Donno, and Champod in Forensic Science Int’l 318 (2021). This paper explores how instances of false positives and false negatives may have resulted from cognitive biases and assumptions formed about the latent print during the examination. The authors discuss some of the differences between mindset in false positives versus false negatives. They end the interview with suggestions for breaking mindset and reducing errors. Link to paper: http://dx.doi.org/10.1016/j.forsciint.2020.110545