Literature DB >> 25984887

Lay understanding of forensic statistics: Evaluation of random match probabilities, likelihood ratios, and verbal equivalents.

William C Thompson1, Eryn J Newman1.   

Abstract

Forensic scientists have come under increasing pressure to quantify the strength of their evidence, but it is not clear which of several possible formats for presenting quantitative conclusions will be easiest for lay people, such as jurors, to understand. This experiment examined the way that people recruited from Amazon's Mechanical Turk (n = 541) responded to 2 types of forensic evidence--a DNA comparison and a shoeprint comparison--when an expert explained the strength of this evidence 3 different ways: using random match probabilities (RMPs), likelihood ratios (LRs), or verbal equivalents of likelihood ratios (VEs). We found that verdicts were sensitive to the strength of DNA evidence regardless of how the expert explained it, but verdicts were sensitive to the strength of shoeprint evidence only when the expert used RMPs. The weight given to DNA evidence was consistent with the predictions of a Bayesian network model that incorporated the perceived risk of a false match from 3 causes (coincidence, a laboratory error, and a frame-up), but shoeprint evidence was undervalued relative to the same Bayesian model. Fallacious interpretations of the expert's testimony (consistent with the source probability error and the defense attorney's fallacy) were common and were associated with the weight given to the evidence and verdicts. The findings indicate that perceptions of forensic science evidence are shaped by prior beliefs and expectations as well as expert testimony and consequently that the best way to characterize and explain forensic evidence may vary across forensic disciplines. (c) 2015 APA, all rights reserved).

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Year:  2015        PMID: 25984887     DOI: 10.1037/lhb0000134

Source DB:  PubMed          Journal:  Law Hum Behav        ISSN: 0147-7307


  3 in total

1.  Deconstructing Cross-Entropy for Probabilistic Binary Classifiers.

Authors:  Daniel Ramos; Javier Franco-Pedroso; Alicia Lozano-Diez; Joaquin Gonzalez-Rodriguez
Journal:  Entropy (Basel)       Date:  2018-03-20       Impact factor: 2.524

Review 2.  Cognitive and human factors in legal layperson decision making: Sources of bias in juror decision making.

Authors:  Lee J Curley; James Munro; Itiel E Dror
Journal:  Med Sci Law       Date:  2022-02-17       Impact factor: 2.051

Review 3.  Juror comprehension of forensic expert testimony: A literature review and gap analysis.

Authors:  Heidi Eldridge
Journal:  Forensic Sci Int       Date:  2019-03-09       Impact factor: 2.395

  3 in total

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