Can Jurors Understand Probabilistic Evidence?

Kaye, David H.; Koehler, Jonathan J.

doi:10.2307/2982696

Cited by 90 publications

(26 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As SIPS scores change gradually, P(M jT i ) values change gradually; Finally, Dr. B's ®ctional SIPS results show that developing a multiple-cut-off interpretation scheme for a malingering measure would be as feasible as was the development of the SIRS, and would not require the``monumental effort'' of examining thousands of subjects, as Rogers and Salekin (1998, p. 153) (Smith, Penrod, Otto, & Park, 1998) suggests, among other things, that jurors may underutilize Bayesian probabilities, a ®nding that accords with previous research (Kaye & Koehler, 1991), and that should assuage some commentators' (e.g., Tribe, 1971) and courts' concerns that frequency statistics will have an overwhelming``impact on the trier of fact'' (State v. Boyd, 1983, p. 482). The issue of how well persons use base rates and probabilistic information is highly complicated and a source of contention among scholars (Continuing Commentary, 1997;Koehler, 1996).…”

supporting

confidence: 62%

The meaning of malingering data: further applications of Bayes' theorem

Mossman

2000

Behavioral Sci & The Law

View full text Add to dashboard Cite

A previous Behavioral Sciences and the Law article (Mossman & Hart, 1996) asserted that information from malingering tests is best conceptualized using Bayes' theorem, and that courts therefore deserve Bayesian interpretations when mental health professionals present evidence about malingering. Mossman and Hart gave several examples of estimated Bayesian posterior probabilities, but they did not systematically address how one constructs confidence intervals for these estimates. This article explains how the usually imperfect nature of humanly created diagnostic tests mandates Bayesian interpretations of test results, and describes methods for generating confidence intervals for posterior probabilities. Sample calculations show that Bayesian reasoning is quite feasible and would not require investigators to expend unusual efforts when constructing and validating malingering instruments. Bayesian interpretations most accurately capture what malingering tests do: provide information that alters one's beliefs about the likelihood of malingering.

show abstract

supporting

confidence: 62%

The meaning of malingering data: further applications of Bayes' theorem

Mossman

2000

Behavioral Sci & The Law

View full text Add to dashboard Cite

show abstract

“…Research has generally found that simulated jurors are less responsive to forensic evidence than Bayesian models indicate they should be (Thompson & Schumann, 1987;Faigman & Baglioni, 1988;Goodman, 1992;Smith et al, 1996;Schklar & Diamond, 1999;Nance & Morris, 2002, 2005Martire et al, 2013Martire et al, , 2014; for reviews of the early studies see Koehler, 2001;Kaye & Koehler, 1991;Thompson, 1989). But Thompson, Kaasa, and Peterson (2013) recently questioned whether jurors always underutilize forensic evidence relative to Bayesian norms.…”

Section: Logical Coherencementioning

confidence: 99%

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

Thompson¹,

Newman²

2015

Law and Human Behavior

View full text Add to dashboard Cite

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 ϭ 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.

show abstract

“…Instead, the evidence suggests that people have a generally poor understanding of probabilities and statistics; tend to confuse likelihoods and posterior beliefs (as in the prosecutor and defence attorney fallacies) 20 ; and tend toward an under-valuing of the evidence as compared to normative (i.e. Bayesian) models [21][22][23][24] . Moreover, meta-analyses of risk communication literature 25 in addition to numerous studies focusing specifically on probabilistic expressions in court [26][27][28][29] suggest that decision-makers are sensitive to the form in which statistics are presented and have a preference for natural frequencies or event rates.…”

Section: Probabilistic Information and Belief Updatingmentioning

confidence: 99%

The psychology of interpreting expert evaluative opinions

Martire

Kemp

Newell

2013

Australian Journal of Forensic Sciences

View full text Add to dashboard Cite

The standards for expressions of evaluative opinions in the forensic sciences are increasingly being challenged and refined. Where once categorical statements regarding the origin of a trace were standard practice, criminalists are now being encouraged to represent the uncertainty associated with their inferential process by using numerical or verbal likelihood ratios. Although there are valid reasons to support this shift, the approach is not without limitations. Decades of psychological research investigating the interpretation and integration of probabilistic expressions, and the equivalence of verbal and numerical formulations for uncertainty, reveals a disconnect between what is intended by experts and what is understood by decisionmakers. In this paper we present an indicative review of the psychological evidence to foster communication and collaboration between forensic scientists and psychologists and reduce instances of miscommunication in our criminal justice system.

show abstract

Can Jurors Understand Probabilistic Evidence?

Cited by 90 publications

References 12 publications

The meaning of malingering data: further applications of Bayes' theorem

The meaning of malingering data: further applications of Bayes' theorem

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

The psychology of interpreting expert evaluative opinions

Contact Info

Product

Resources

About