David H. Kaye

Can Jurors Understand Probabilistic Evidence

Some courts have been reluctant to admit testimony expressing probabilities because of a concern that jurors will overweight it relative to other evidence. However, empirical studies indicate a tendency to underweight statistical evidence when other sources of evidence are available. For more than two decades, researchers have studied the ways that people process probabilistic and statistical information, but only a small portion of these studies focuses on the capacity of jurors to process explicitly quantitative probabilistic evidence. This paper reviews this research. It concludes that the work has produced several insights into the factors that affect the judgments of mock jurors, and that it is valuable in devising optimal rules for the admission or exclusion of probability evidence. At the same time, we do not believe that the experiments published to date have been adequate in their design and implementation to demonstrate unequivocally the extent to which jurors attend to trace evidence or to identify what decision aids, if any, would promote an appropriate weighting of the evidence at trial.

Statistical Evidence of Discrimination

Abstract Generally speaking, laws against discrimination prohibit treating similarly situated persons differently. Legal analysis or social policy supplies the criteria for ascertaining which persons are similarly situated. Statistical evidence has been used in cases, among others, alleging discrimination in criminal prosecutions, educational opportunities, jury selection, and employment practices. The United States Supreme Court has been ambivalent about the need for significance testing in such cases, and it has yet to consider carefully the use of formal inferential methods. Various techniques for conveying information to a court about the inferential value of sample statistics are illustrated in the context of two jury-selection discrimination cases. The role of statistical evidence in certain employment discrimination cases is also considered. It is suggested that the classical method of hypothesis testing used by the Supreme Court is not appropriate to testing whether a given defendant discriminated...

Science in the jury box: jurors' comprehension of mitochondrial DNA evidence.

Questions about how jurors understand and apply scientific evidence were addressed in a mock jury study in which 480 jury pool members watched a videotaped mock trial that included expert testimony about mitochondrial DNA (mtDNA) evidence purportedly linking a defendant to a crime. Collectively, jurors showed moderately good comprehension of the mtDNA evidence, although some made definitional and inferential errors. Comprehension was better among jurors with higher educational attainment and more mathematics and science courses. Lower comprehension was associated with jurors’ reservations about science and concerns about the contamination of mtDNA evidence. The results suggest that most jurors are capable of comprehending and employing scientific evidence presented during trial, although errors and doubts about the evidence should be anticipated.

The Misquantification of Probative Value

D. Davis and W. C. Follette (2002) purport to show that when “the base rate” for a crime is low, the probative value of “characteristics known to be strongly associated with the crime ... will be virtually nil.” Their analysis rests on the choice of an arbitrary and inapposite measure of the probative value of evidence. When a more suitable metric is used (e.g., a likelihood ratio), it becomes clear that evidence they would dismiss as devoid of probative value is relevant and diagnostic.

Clarifying the Burden of Persuasion: What Bayesian Decision Rules Do and Do Not Do

This article articulates and analyses the assumptions and reasoning behind the decision-theoretic explication of the burden of persuasion. In doing so, it responds to Professor Ronald J. Allens claim that the “the various proofs that employing the civil burden of persuasion of a preponderance of the evidence will minimize or optimize errors ... are all false as general proofs” because “[t]hey neglected base rates and the accuracy of probability assessments of liability ... .” I show that this criticism suffers from a failure to distinguish between expected and actual errors. A proof that a given decision rule minimizes the expected value of a prescribed loss function remains true for all possible base rates. To establish this elementary point, the article presents one such proof and a few numerical examples that illustrate the sense in which the more-probable-than-not standard is optimal. This exercise clarifies both the premises and conclusions of the decision-theoretic analysis of the civil burden of persuasion. Describing the mathematical reasoning carefully should lay to rest common misconceptions about the properties of such rules and helps indicate how the evidentiary analysis fits into a broader framework of economic and legal analysis.

The Good, the Bad, the Ugly: The NAS Report on Strengthening Forensic Science in America

In the long run, the NRC report can only have a salutary effect on forensic science. Although the report is not exhaustive in its review of the relevant literature and the law, and although broad constituencies may never embrace its most radical proposals, the report exposes the soft underbelly of a range of technologies, the organizational problems with the institutions that generate forensic science evidence, and the timidity of the courts in pushing for better science. Even if the full promise of the report is not realized, its publication ultimately should strengthen the good in a system of law and science that has its fair share of the good, the bad, and the ugly.

Bayes Wars Redivivus - An Exchange

An electronic exchange among 10 evidence scholars that began with a discussion of the restyled Federal Rules and grew into a significant restatement of debates in evidentiary scholarship over the last 50 years, touching on relevance, probative value, inference, Bayesianism and the foundations of evidence, with an introduction by Michael Risinger.

Bayes, Burdens and Base Rates

Evidence has also experienced the demise of legal theorems. The best example is the proofs that employing the civil burden of persuasion of a preponderance of the evidence will minimise or optimise errors. These are all false as general proofs (although not as special cases), and all for the same reasons. They neglected base rates and the accuracy of probability assessments of liability, and virtually any relationship at all can exist between subjective assessments of liability and the truth of factual assertions at trial.’

Probability Theory Meets Res Ipsa Loquitor

Day in and day out, attorneys, judges, and jurors must estimate probabilities. To be sure, we rarely quantify such estimates of probability and almost never adopt the terminology and mathematics of probability theory to resolve matters. Nevertheless, the mathematical theory of probability can be applied to legal problems in various ways. This article uses probability theory normatively in an effort to clarify one aspect of the famous tort doctrine known as res ipsa loquitur. While not urging that jurors be instructed in probability theory or be equipped with microprocessors, it does seek an accurate statement of the res ipsa doctrine in ordinary language. In particular, it shows that the conventional formulation of the doctrine is misleading at best, and should be replaced with a more careful statement of the conditions warranting the res ipsa inference. It briefly surveys the aspect of the legal doctrine criticized, develops a mathematical apparatus and uses it to expose the weakness in the current version of res ipsa loquitur, and summarizes and elaborates on conclusions that suggest which cases should reach a jury and what instructions the jury should receive.

DNA Probabilities in People v. Prince: When Are Racial and Ethnic Statistics Relevant?

When a defendants DNA matches a sample found at a crime scene, how compelling is the match? To answer this question, DNA analysts typically use relative frequencies, random-match probabilities or likelihood ratios. They compute these quantities for the major racial or ethnic groups in the United States, supplying prosecutors with such mind-boggling figures as one in nine hundred and fifty sextillion African Americans, one in one hundred and thirty septillion Caucasians, and one in nine hundred and thirty sextillion Hispanics. In People v. Prince, a California Court of Appeals rejected this practice on the theory that only the perpetrators race is relevant to the crime; hence, it is impermissible to introduce statistics about other races. This paper critiques this reasoning. Relying on the concept of likelihood, it presents a logical justification for referring to a range of races, and it identifies some problems with the one-race-only rule. The paper also notes some ways to express the probative value of a DNA match quantitatively without referring to variations in DNA profile frequencies among races or ethnic groups.