Further Readings
Hanson, R. K., & Thornton, D. (2000). STATIC–99:
Improving risk assessments for sex offenders: A
comparison of three actuarial scales. Law and Human
Behavior, 24,119–136.
Hanson, R. K., & Thornton, D. (2003). Notes on the
development of STATIC–2002. User Report 2003–01.
Ottawa, ON, Canada: Department of the Solicitor General
of Canada.STATISTICALINFORMATION,
IMPACT ONJURIES
Statistical information is increasingly likely to be pre-
sented in court. It may appear in civil cases (e.g., per-
centages of men and women employees in a gender
discrimination case) or criminal cases (e.g., the defen-
dant’s blood type matches that of a sample found at
the crime scene and that blood type is found in only
20% of the population). Can jurors understand that
information on their own, or must they rely on experts
to explain its meaning? Even if jurors correctly under-
stand statistical evidence, how do they combine that
evidence with other, nonquantitative evidence?
In contrast to other areas of juror understanding
(e.g., juror beliefs about factors affecting the accuracy
of eyewitness identification), there is relatively little
research directly answering these questions. Those
studies can be broken into two broad categories. The
first focuses primarily on understanding of the statisti-
cal evidence. The second asks how statistical evidence
is combined with other nonstatistical evidence.
Considered together, jurors have some difficulty under-
standing even a single piece of statistical evidence. That
difficulty increases when faced with two pieces of sta-
tistical evidence. Jurors also tend to underuse statistical
evidence, when compared with a Bayesian norm, even
when provided with instructions on how to use such
evidence. That underuse, however, conceals consider-
able variation.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 nakedstatistics compared with mathematically equivalent evi-
dence 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 com-
pany’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 condi-
tion 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 esti-
mates 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 infor-
mation 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 argu-
ment. An attorney who makes such an argument in the
face of expert testimony (e.g., when the expert explains762 ———Statistical Information, Impact on JuriesS-Cutler (Encyc)-45463.qxd 11/18/2007 12:44 PM Page 762