Science of Justice
Our science, your art.
You've got the vision; we've got the data.
Is our science the right fit for your practice? Is the earth round? Let’s find out. We have created a unique suite of machine intelligence solutions that provide you with the best information in your legal cases. We explore insightful results through our proprietary algorithms with experts with decades of experience working with behavioral science issues or collaborating with legal advisors for successful case outcomes.
Science of Justice
What Lawyers Miss When They Skip the Science
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Jury Analyst
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Season 2
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Episode 36
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- Explores why internal agreement inside the trial war room often creates false confidence rather than real trial readiness.
- Breaks down the hidden psychological forces shaping plaintiff trial strategy, including confirmation bias, hierarchy bias, and overconfidence.
- Examines the critical gap between internal clarity (how lawyers understand a case) and juror clarity (how real decision-makers process it).
- Reveals why traditional focus groups and demographic profiling frequently produce misleading signals about verdict risk.
- Introduces psychographic analysis as a method for identifying hidden resistance profiles before jurors ever enter deliberations.
- Demonstrates how elite plaintiff teams move from opinion-based preparation to measurable behavioral analytics.
- Explains deliberation modeling and how persuasion decay can dismantle even strong plaintiff narratives behind closed jury-room doors.
- Shows how social dominance dynamics influence verdict outcomes more than initial juror opinions.
- Outlines how structured dissent and red-team analysis eliminate internal echo chambers within trial teams.
- Reframes trial preparation as behavioral systems engineering — shifting from instinct-driven advocacy to calibrated decision strategy.
- Provides a practical framework for pressure-testing case themes, witnesses, and damages arguments before trial begins.
- Challenges trial teams to replace confidence built on consensus with confidence grounded in measurable risk signals.
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