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The over-citation of Daubert in forensic anthropology
Authors:Kate M Lesciotto JD  PhD  Angi M Christensen PhD
Institution:1. University of North Texas Health Science Center, Fort Worth, Texas, USA;2. Federal Bureau of Investigation, Laboratory Division, Quantico, Virginia, USA
Abstract:The 1993 US Supreme Court decision Daubert v. Merrell Dow Pharmaceuticals, Inc. presented new guidance for the judicial assessment of expert witness evidence and testimony in the determination of admissibility. Despite the rarity of admissibility challenges to forensic anthropology evidence, Daubert is frequently cited in published forensic anthropology research. This study undertook a qualitative thematic analysis of forensic anthropology articles published in the Journal of Forensic Sciences to assess why authors continue to cite Daubert and express concerns over potential exclusion. The results show a significant increase in the number of articles that cite legal admissibility standards over time (p < 0.001). Authors frequently cite these standards to contextualize their results within the Daubert framework or to justify the need for their research. Notably, many articles present Daubert as a constraining force, misinterpreting the guidelines as rigid criteria or that they require methods to be strictly quantitative. However, Daubert was intended to be a flexible tool for judges—not a standard or instruction for scientists. While it was reasonable to reflect on the scientific rigor of methods in the wake of the Daubert decision, a new perspective is warranted in which forensic anthropologists shift their focus from trying to “satisfy” admissibility guidelines to adopting quality assurance measures that minimize error and ensure confidence in analytical results, and developing and using methods that are grounded in good science—which is important regardless of whether or not the results are ever the subject of a trial.
Keywords:Daubert  expert witness evidence  forensic anthropology  legal admissibility standards  qualitative data  quality assurance  quantitative data
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