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What Jurors Hear When DNA Experts Testify: Results From Controlled Experiments Jonathan J. Koehler McCombs School of Business The University of Texas at Austin email@example.com Forensic Bioinformatics 5th Annual Conference Dayton, OH August 13, 2006 .
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Results From Controlled Experiments
Jonathan J. Koehler
McCombs School of Business
The University of Texas at Austin
Forensic Bioinformatics5th Annual Conference
August 13, 2006
A scientific method for testing hypothesized causal relationships between independent and dependent variables
Randomly assign subjects to groups
Manipulate independent variables
Measure dependent variables
“[T]he overwhelming majority of studies that have directly compared different mock juror samples have failed to find consistent differences…[There is] strong evidence that factors at trial affect students and nonstudents in the same way.”
(Bornstein, 1999, p. 80)
Simple Written Materials (1 page case summaries)
Detailed Written Materials (opening arguments, witness statements, closing arguments, judicial instructions, deliberation)
Detailed Video Materials (videotaped trials based on actual cases, deliberation)
“Studies that have directly compared presentation media [e.g., written summaries, transcripts, audiotape, videotape] – for either a whole trial or a portion of testimony – fail to offer consistent findings. . . . [R]esearch on the trial medium tends not to find many differences.”
(Bornstein, 1999, p. 82 & 84)
The results of controlled studies (i.e., experiments) with mock
jurors can provide insight into how real jurors think and respond at trial.
The weight that people attach to statistical evidence is influenced by whether or not they can easily imagine examples of the event in question.
There are different ways to describe the chance of winning the daily three-digit New York Lottery Numbers game:
1. One in every 1,000 tickets out of the 500,000 tickets that are sold each day will win.
2. There is a 0.1% chance that your ticket will win.
Frequency Form: 1 in 1,000
Probability Form: 0.1%
Odds Form: 1:999
Multiple Target (examples: yes)
Single Target (examples: no)
“0.1% of the 500,000 tickets sold each day will win” [multiple]
“There is a 0.1% chance that your ticket will win” [single]
Statistical Form and Target influence whether jurors find it easy or hard to imagine examples of others who might match.
Easy to imagine examples of other matches?
1. Probability Form + Single Target
“The probability that Mr. Clinton would match the semen stain if he were not its source is 0.1%”
2. Frequency Form + Multiple Target
“1 in 1,000 people in Washington D.C. who are not the source would also match the semen stain”
Hardware store owner shot and killed during robbery
Killer wore a mask
Killer bled at crime scene
Several neighborhood residents gave blood samples
One suspect matched partial PCR DNA profile
(RMP = 1 in 100,000)
No corroborating evidence
Form (probability, frequency)
Target (single, multiple)
Reference Class Size (small, large)
Probability of Source
Probability of Guilt
RMP & Reference Class Size affect whether examples of coincidental matches will be easy or hard to imagine.
RMP Reference Class Size Examples?
1 in 1,000 5,000,000 Yes
1 in 1,000,000 5,000,000 Yes
1 in 1,000,000,000 5,000,000 No
Implication: The way RMP is presented matters less as RMP becomes very small.
Expert: My tests cannot rule out [the suspect] as a possible source of the recovered genetic material. Approximately [X] out of [Y] people share this DNA profile, and the suspect is one of those people.
RMP = 1 in 100,000
A. 0.1 out of 10,000 people
B. 1 out of 100,000 people
C. 2 out of 200,000 people
RMP = 1 in 1,000
A. 0.1 out of 100 people
B. 1 out of 1,000 people
C. 2 out of 2,000 people
DNA evidence can be presented in odds form as the ratio of 2 conditional probabilities, when a few assumptions are made.
This is the Likelihood Ratio (LR)
The LR is a term that appears in a mathematical formula called Bayes Theorem.
77% of people verbally confuse LRs with Posterior Odds Ratios (Wolfe, 1995)
Most people reason more accurately with frequencies than with conditional probabilities (Cosmides & Tooby, 1996)
Some people equate P(H|D) with P(H&D) (Gigerenzer & Hoffrage, 1995) or with P(D|H) (Thompson, 1989)
Experts Make Errors When Explaining the meaning of a LR
LR = 14,961 [State of Texas v. Griffith, 1996]
Expert: “Given this evidence, it is 14,961 times more likely that the defendant is the father than a random man.”
Proper Statement (LR): "It is 14,941 times more likely that we would see this evidence if the defendant were the father than if the defendant were not the father.“
Improper Statement (Posterior Odds): “Given this evidence, it is 14,941 times more likely that the defendant is that father than that the defendant is not the father.“
Is it reasonable to expect that jurors will understand that these two statements are different?
1. Frequency: Approximately 1 person out of every 1000 would yield a DNA match with the semen and the defendant is one such person.
2. LR: It is approximately 1000 times more likely we would see this DNA match if the defendant is the source of the semen than if the defendant is not the source of the semen."
3. Posterior Odds Ratio: Given that we see this DNA match, it is approximately 1000 times more likely that the defendant is the source of the semen than that he is not the source of the semen.
Jurors are more persuaded by a LR than by a frequency
Jurors respond to a LR just as they respond to a posterior odds ratio.
Conclusion: Jurors think that a likelihood is a posterior.
Do jurors understand how to integrate error rates with RMPs?
False Positive Error Rate = .02 (also used .001)
RMP = 1 in 1,000,000,000
1. Error rate only
2. RMP only
3. Error Rate + RMP (separately)
The way DNA RMPs are presented matter
Easy to think of examples: Good for Defense
Hard to think of examples: Good for Prosecution
LRs are misunderstood
DNA error rates are often ignored
When not ignored, jurors are not sure how to
Schklar & Diamond (1999)
Jurors usually underweight DNA evidence
Jurors overweight DNA evidence when given separate estimates
for the RMP and error rate
- overweighting persists with aggregation instruction
Nance & Morris (2005)
Aggregation instruction for RMP + error rate has no effect
LR approach yields higher conviction rate