Statistical Information, Impact on Juries

Juror Understanding of Statistical Evidence

“Naked statistics” (sometimes referred to as base rates) are data that are true, regardless of what happened in a particular case. Mock jurors are not persuaded by naked statistics compared with mathematically equivalent evidence that is contingent on some ultimate fact (i.e., a fact essential to resolution of the case). For example, in the Blue Bus problem, a bus runs over a color-blind woman's dog. The defendant, Company A, owns 80% of the buses in the area, and all of Company A's buses are blue. Company B owns 20% of the buses, and its buses are gray. The color-blind woman cannot tell a blue bus from a gray bus, so she does not know which company's bus ran over her dog. She sues Company A on the theory that, because Company A owns 80% of the buses in the area, there is an 80% chance that a Company A bus killed her dog. In experiments, jurors in one condition hear that the defendant owns 80% of the buses in the area, while those in another condition hear an 80%accurate weigh-station attendant's identification of the defendant bus company. Both sets of jurors believe it equally probable that the defendant's blue bus, rather than Company B's gray bus, killed the dog. But only jurors who heard the attendant's testimony are willing to find against the bus company. Jurors who simply heard the naked statistics (Company A owns 80% of the buses) do not find Company A responsible. Similarly, although learning that the defendant is responsible for 80% of the accidents in the county leads to high probability estimates that the defendant's bus killed the dog, jurors are unwilling to find the defendant responsible.

Most research has examined “nonnaked” statistical information—information in which one's belief about the ultimate fact (in the example above, whether or not a blue bus hit the dog) is linked to one's belief about the evidence (the weigh-station attendant's accuracy). Some research finds that the manner in which statistical information is presented may affect mock jurors' use of the information. For example, incidence rate information presented in the form of a conditional probability (there is only a 2% probability that the defendant's hair would match the perpetrator's if the defendant were innocent) may encourage some jurors to commit the prosecutor's fallacy. These jurors believe that there is a 98% chance that the defendant is guilty. If the same information is presented as a percentage and number (a 2% match in a city of 1,000,000 people, meaning 20,000 people share that characteristic), some others may commit the defense attorney's fallacy. They believe the evidence shows only a 1 in 20,000 chance that the defendant is the culprit. These errors may be more likely when an expert, rather than an attorney, offers the fallacious argument. An attorney who makes such an argument in the face of expert testimony (e.g., when the expert explains [Page 763]Bayes's theorem) runs the risk of backlash; the defense attorney's fallacy combined with expert Bayesian instruction may increase guilty verdicts.

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