From Gut Feel to Juror Science: How Data Quality Decides Plaintiff Outcomes

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We argue that pretrial research only works when the data is venue-specific, scientifically vetted, and integrated end-to-end. We show how bad samples lead to undervaluing or overestimating cases, and how psychometrics, experimental design, and hyperlocal platforms sharpen strategy and jury selection.

• stakes of pretrial data quality for plaintiffs
• two core risks of flawed research undervaluing and overconfidence
• seven common mistakes in focus groups and simulations
• venue mismatch and why prediction is not the goal
• groupthink, social desirability, and false confidence
• measuring hidden biases with validated scales like locus of control and BJW
• signal vs noise and weighting patterns across groups
• psychographics shaping SJQs and voir dire strategy
• integrating insights into discovery, depositions, and narrative
• why convenience sampling fails and what to use instead
• purposeful recruitment, rigorous screening, and oversamples
• facial microexpressions, linguistic cues, and experimental sequencing
• hyperlocal proprietary data and actionable juror risk scoring

Invest in validated, venue-specific research now. Make smart data the cornerstone of your next case.

https://scienceofjustice.com/

@JuryAnalyst