Course Objective & Scope
Not every chart, vendor benchmark or AI research summary brought to a meeting deserves equal weight; tiny sample sizes, cherry-picked timeframes, survivorship bias and mistaking correlation for causation regularly derail corporate decisions. This course equips leaders and professionals with practical evidence literacy—without requiring a degree in statistics.
Participants dissect real business reports, vendor claims, metric dashboards and LLM outputs to score evidence quality and build an internal Evidence Verification Checklist.
Who Should Attend?
- Managers and directors who rely on reports and data to make commercial or operational decisions
- Strategy, product management, marketing, procurement and business development teams
- Analysts and specialists using AI tools for market and competitive research
- No advanced statistics background required
Key Takeaways
- Rank claims along the hierarchy of evidence from anecdotes and surveys to controlled experiments
- Spot survivorship bias, selection bias, cherry-picked baselines and base-rate neglect in reports
- Challenge spurious correlations and identify hidden confounding variables
- Verify AI-generated research and citations through source triangulation
- Apply a structured Evidence Quality Rubric before approving business cases
Curriculum
The Hierarchy of Business Evidence: From Anecdote to Experiment
- Weighing single anecdotes, surveys, vendor whitepapers, observational data and A/B tests
- Auditing source incentives: asking who generated the number and why
Statistical Traps: Sampling, Base Rates and Survivorship Bias
- Survivorship bias, self-selection bias and small-sample mirages
- Relative versus absolute percentages, mean versus median and base-rate neglect
Correlation, Causation and Confounding Variables
- Why co-moving metrics rarely prove cause and effect
- Reverse causality, hidden confounders and cherry-picked date ranges
Verifying AI Research Outputs and Secondary Sources
- Detecting fabricated citations, outdated benchmarks and confident hallucinations in LLM outputs
- Tracing claims back to primary data and triangulating across independent sources
Evidence Audit and Verification Rubric Workshop
- Forensic review exercise: finding 10 flawed evidence claims in sample business proposals
- Creating a reusable Evidence Quality Rubric for team decision memos
Other Courses in This Category
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Defining weighted decision criteria (MCDA) before evaluating options, separating knockout must-haves from scored trade-offs and building decision matrices on a real organisational choice.
- Live Course
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Overcoming brittle single-point forecasts with 2x2 scenario matrices, early-warning signposts, trigger-based contingency plans and Gary Klein's Pre-Mortem method on a real initiative.
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Using LLMs as structured devil's advocates and Red Team partners rather than sycophantic yes-men; covers steelmanning, assumption hunting and stress-testing strategic proposals before approval.
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Defining the real root problem instead of chasing symptoms; covers the Problem Statement Canvas, MECE issue trees, 5 Whys, scope boundaries and aligning stakeholders before jumping to solutions.
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Hypothesis-driven analysis, argument mapping (claim-evidence-warrant), deductive and inductive reasoning, spotting logical fallacies and assumption mapping; participants solve business cases systematically.
- Live Course
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Kahneman's System 1/System 2 thinking, confirmation bias, sunk cost fallacy, anchoring, groupthink and decision noise; teams design practical debiasing checklists and decision hygiene routines.
- Live Course
- 5 modules
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