This page offers a curated selection of tools and knowledge to help you understand the key principles behind our jury and juror analysis services. Here, you’ll find a glossary of important terms related to survey research and hypothesis testing, recommended readings, links to our recent publications, and classic works on jurors and juries. Our aim is to equip you with the insights and understanding needed to make data-driven decisions in your litigation strategies.
Glossary of Key Terms
Understanding the language of survey research and data analysis is crucial for making informed decisions. Our glossary covers essential terminology used in our jury analysis process.
- Confidence intervals: A range of values, derived from sample data, that is likely to contain the true population parameter. A confidence interval represents the margin of error and can often be depicted on a graph to communicate uncertainty effectively.
- Double blind: A method of assigning experiment participants to experimental conditions where neither the participant nor the research know what condition the participant is assigned to. Double blind assignments address the possibility that participants will behave differently knowing their role in an experiment.
- Hypothesis testing: A statistical method used to determine whether there is enough evidence in a sample to infer a particular outcome for the population.
- Jury-qualified adults: Adults who meet qualifications for serving on juries in the United States, such as U.S. citizenship, no felony convictions, and physical and mental ability to serve on jury. There are additional qualifications in death penalty cases.
- Margin of error: An expression of the range within which we expect the true population parameter to fall, based on the survey results.
- Randomized experiment: A research method where the research randomly assigns participants to experimental conditions with varying levels of some causal variable hypothesized to affect measurable outcomes. Randomized experiments are considered the gold standard of proof because the design ensures that the only difference between experimental groups is the causal variable and nothing else explains observed differences in measured outcomes.
- Reliability: The consistency of a measurement or survey tool. A reliable survey will produce the same results under consistent conditions. In jury analysis, reliability ensures that your methods yield stable, repeatable results over time.
- Sampling frame: The population from which a sample is drawn. It represents the population you wish to study. In our research, the sampling frame excludes adults who do not meet basic jury qualifications, even though they may be part of the sampling frame for other research purposes.
- Sample size: The number of individuals surveyed to draw conclusions about the larger population.
- Statistic vs. Parameter: A statistic is a numerical value calculated from a sample, such as the mean or proportion. A parameter, on the other hand, is a numerical value that describes a characteristic of the entire population. While a statistic is used to estimate a parameter, the true parameter is often unknown.
- Statistical power: The probability that a test will detect a true effect or difference when it exists. High statistical power means there’s a lower chance of missing a real effect, which helps ensure that your analysis produces reliable results. Power is affected by factors such as sample size, effect size, and significance level.
- Statistical significance: The determination whether the results of a study could have occurred by random chance, as opposed to a genuine effect or difference. A result is statistically significant if it passes a pre-determined threshold (usually 0.05), suggesting that the observed effect is likely genuine.
- Validity: The extent to which a measurement is an unbiased measure of what it is intended to measure. In the context of jury analysis, a valid survey accurately reflects jurors’ attitudes or likely behaviors in relation to a case.
SATE Package for R Statistical Software
Our analysis uses SATE: Scientific Analysis of Trial Errors, a suite of functions that we developed and made freely available to the public to make our analysis methods transparent and easy for others to replicate. Suggested resources about R:
- An R Companion to Political Analysis by Philip Pollock, III and Barry C. Edwards
- Computational Statistics by Geof H. Givens and Jennifer A. Hoeting
- R for Data Science by Hadley Wickham and Garrett Grolemund
- R Programming for Data Science by Roger D. Peng
- Simulation for Data Science with R by Matthias Templ
- The R Book by Michael J. Crawley
Recent Publications by Dr. Barry Edwards
Explore the latest research and insights from Dr. Barry Edwards, a leader in the field of jury analysis and legal strategy.
- “Measuring Fairness,” Alabama Law Review (forthcoming Fall 2025)
- “If the Jury Only Knew: The Effect of Omitted Mitigation Evidence on the Probability of a Death Sentence,” Virginia Journal of Social Policy & the Law (forthcoming Spring 2025).
- The Essentials of Political Analysis, 7th Edition (Sage Publications, 2025) (with Philip H. Pollock III).
- “Scientific Framework for Analyzing the Harmfulness of Trial Errors,” 8 UCLA Criminal Justice Law Review 1 (2024).
Recommended Readings about Jurors and Juries
We’ve compiled a list of recommended books and articles about survey research and the scientific approach to hypothesis testing.
- Jury Decision Making: The State of the Science by Dennis J. Devine
- Inside the Juror: The Psychology of Juror Decision Making by Reid Hastie
- The American Jury by Harry Kalven and Hans Zeisel – a classic text exploring how juries decide cases.
- The Jury System: Insights from Social Science and the Law by Neil Vidmar and Valerie Hans
- Trial by Jury: Understanding Juror Decision Making by Valerie Hans
Recommended Readings about Survey Research and Hypothesis Testing
- Designing Surveys: A Guide to Decisions and Procedures by Johnny Blair, Ronald F. Czaja, and Edward A. Blair
- Hypothesis Testing: Principles and Practice by Rimantas Petrauskas and Kevin Reilly
- Statistical Methods for the Social Sciences by Alan Agresti and Barbara Finlay
- Survey Methodology by Robert M. Groves, Floyd J. Fowler Jr., Mick P. Couper, James M. Lepkowski, Eleanor Singer, and Roger Tourangeau
- The Art of Asking Questions by Stanley L. Payne
- The Essentials of Political Analysis by Philip Pollock, III and Barry C. Edwards