Analytical Thinking and Evidence-Based Decision Making Training Course

Participants can define decision-worthy problems and investigate plausible underlying causes.

• SMART problem definitions specify observable conditions, boundaries, consequences and owners
• 5 Whys traces symptoms toward controllable causal mechanisms
• Fishbone diagrams classify potential causes under people, process, technology and policy
• Current-state process maps reveal handoffs, rework, delays and control points
• Pareto charts rank recurring issues by frequency, cost or risk
• Problem framing separates decision questions from research questions and actions

Participants can assess whether available evidence is relevant, credible and sufficient for a decision.

• Evidence hierarchies compare systematic reviews, experiments, observational studies and expert judgement
• Source evaluation tests authority, currency, relevance, methodology and conflicts of interest
• Triangulation corroborates claims using independent qualitative, quantitative and operational sources
• Fact, interpretation and recommendation labels prevent unsupported conclusions
• Evidence logs record provenance, limitations, confidence and follow-up questions
• Data collection plans align measures, population, timing, access and ethics

Participants can interpret common operational data accurately and detect misleading presentations.

• Operational definitions specify measures, units, thresholds, inclusions and exclusions
• Descriptive statistics summarise distributions using mean, median, range and percentiles
• Data quality checks identify missing values, duplicates, outliers and inconsistent codes
• Correlation coefficients indicate association without establishing causal direction
• Base rates and denominators prevent misleading comparisons between groups
• Charts selected by question avoid distorted axes and inappropriate aggregation

Participants can recognise decision traps and communicate uncertainty without delaying necessary action.

• Confirmation bias, anchoring and availability bias distort evidence interpretation
• Pre-mortem analysis identifies failure modes before selecting a course
• Confidence intervals communicate estimate precision rather than false certainty
• Sensitivity analysis tests conclusions against changing assumptions and inputs
• Bayesian updating revises confidence as new evidence becomes available
• Red-team review challenges preferred explanations, data gaps and inferential leaps

Participants can analyse interconnected causes, feedback effects and unintended consequences.

• Systems maps show feedback loops, delays, dependencies and unintended consequences
• Causal loop diagrams distinguish reinforcing and balancing feedback structures
• Root cause analysis tests causal chains against observed process evidence
• Fault tree analysis traces adverse outcomes through logical contributing events
• Influence diagrams connect decisions, uncertainties, objectives and information needs
• Counterfactual questions test whether proposed causes change outcomes

Participants can compare alternatives using explicit criteria, trade-offs and uncertainty assumptions.

• Decision criteria translate objectives into observable, weighted assessment measures
• Weighted decision matrices compare alternatives against agreed criteria and evidence
• Cost-benefit analysis records financial, operational, social and compliance consequences
• Opportunity cost identifies benefits forgone when resources support one option
• Decision trees model choices, probabilities, payoffs and contingent actions
• Threshold analysis identifies conditions under which the preferred option changes

Participants can establish fair, accountable decision processes that accommodate legitimate challenge.

• Stakeholder maps identify decision makers, affected groups, influencers and dissenting voices
• RAPID decision roles clarify recommend, agree, perform, input and decide accountabilities
• Ethical screens examine fairness, harm, privacy, transparency and proportionality
• Conflict-of-interest checks protect evidence selection and recommendation credibility
• Structured dissent surfaces minority evidence before final commitment
• Accessibility and inclusion considerations test differential impacts of proposed decisions

Participants can present concise recommendations that decision makers can inspect and challenge.

• A3 reports connect problem, evidence, analysis, recommendation, risks and follow-up
• Pyramid Principle structures executive messages from conclusion to supporting evidence
• Claim-evidence-reasoning chains make recommendations traceable and challengeable
• Decision briefs distinguish known facts, assumptions, uncertainties and required approvals
• Data visualisation annotations state source, timeframe, denominator and material caveats
• Recommendation narratives present alternatives considered and reasons for rejection

Participants can establish a repeatable process for reviewing decisions and improving future judgement.

• Decision journals capture context, assumptions, expected outcomes and review dates
• After-action reviews compare expected results, actual results, causes and adjustments
• Outcome measures distinguish leading indicators, lagging indicators and unintended effects
• Personal decision protocol defines evidence thresholds, consultation points and escalation triggers
• Action plans assign decisions, data owners, milestones and review cadences
• Learning loops convert decision results into revised standards and practices