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Incorporating Inclconlsuve Outcomes in Error Rate Estimation with Applications in Forensic Science

Abstract Number:

2737

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Paper

Participants:

Sydney Campbell (1), Karen Kafadar (1), Jordan Rodu (1)

Institutions:

(1) University of Virginia, Charlottesville, VA

Co-Author(s):

Karen Kafadar
University of Virginia

Jordan Rodu
University of Virginia

First Author:

Sydney Campbell
University of Virginia

Presenting Author:

Sydney Campbell
N/A

Abstract Text:

Binary decision-making occurs in many areas of science and policy; e.g., medicine (tumor present or absent), forensics (ID or exclusion), finance (good or bad credit risk), and agriculture (healthy or diseased plant). Lab or field studies may be conducted to assess the error rates in such binary decision-making processes (e.g., proficiency tests for radiologists or latent print examiners). In such tests, a true outcome is known (e.g., latent print and file print did or did not come from the same source), but study outcomes allow three responses (e.g., ``same,'' ``different,'' ``inconclusive''). Many forensic science articles report such studies' results by completely ignoring inconclusive decisions, which can artificially increase the apparent error rate. In this talk, we propose a weighting scheme to incorporate inconclusive decisions into error rates stratified by latent print quality. Additionally, we propose that Standardization can be used to compare error rates across labs and studies.

Keywords:

Sponsors:

Survey Research Methods Section

Tracks:

Weighting/Variance Estimation

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