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If the Shoe Fits They Might Acquit: The Value of Forensic Science Testimony

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  • Jonathan J. Koehler

Abstract

The probative value of forensic science evidence (such as a shoeprint) varies widely depending on how the evidence and hypothesis of interest is characterized. This article uses a likelihood ratio (LR) approach to identify the probative value of forensic science evidence. It argues that the “evidence” component should be characterized as a “reported match,” and that the hypothesis component should be characterized as “the matching person or object is the source of the crime scene sample.” This characterization of the LR forces examiners to incorporate risks from sample mixups and examiner error into their match statistics. But will legal decisionmakers be sensitive to the extent to which examiners' statistical testimony accounts for various potential sources of risk and error? A controlled experiment with 315 jury‐eligible jurors who received a shoeprint match statistic in a hypothetical burglary case finds that, contrary to normative theory, people are more persuaded by statistical testimony that ignores various error risks than by testimony that is objectively stronger by virtue of taking those risks into account. The experiment also finds that jurors are relatively unresponsive to exposure of those risks by a defense attorney on cross‐examination. These results support and extend previous research that finds many people are confused about how to evaluate the risk of error associated with expert forensic testimony.

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  • Jonathan J. Koehler, 2011. "If the Shoe Fits They Might Acquit: The Value of Forensic Science Testimony," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 8(s1), pages 21-48, December.
    • Handle: RePEc:wly:empleg:v:8:y:2011:i:s1:p:21-48
    DOI: 10.1111/j.1740-1461.2011.01225.x
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    References listed on IDEAS

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    1. Ronald J. Allen & Michael S. Pardo, 2007. "The Problematic Value of Mathematical Models of Evidence," The Journal of Legal Studies, University of Chicago Press, vol. 36(1), pages 107-140, January.
    2. David H. Kaye & Valerie P. Hans & B. Michael Dann & Erin Farley & Stephanie Albertson, 2007. "Statistics in the Jury Box: How Jurors Respond to Mitochondrial DNA Match Probabilities," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 4(4), pages 797-834, December.
    3. Dale A. Nance & Scott B. Morris, 2005. "Juror Understanding of DNA Evidence: An Empirical Assessment of Presentation Formats for Trace Evidence with a Relatively Small Random-Match Probability," The Journal of Legal Studies, University of Chicago Press, vol. 34(2), pages 395-444, June.
    4. I. W. Evett, 1984. "A Quantitative Theory for Interpreting Transfer Evidence in Criminal Cases," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 25-32, March.
    5. Silvia Bozza & Franco Taroni & Raymond Marquis & Matthieu Schmittbuhl, 2008. "Probabilistic evaluation of handwriting evidence: likelihood ratio for authorship," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 329-341, June.
    6. D. H. Kaye & Jonathan J. Koehler, 1991. "Can Jurors Understand Probabilistic Evidence?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(1), pages 75-81, January.
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    Cited by:

    1. Brandon Garrett & Gregory Mitchell, 2013. "How Jurors Evaluate Fingerprint Evidence: The Relative Importance of Match Language, Method Information, and Error Acknowledgment," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 10(3), pages 484-511, September.

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