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Criterion
A hypothetical failure makes concrete risks, signals, and fitting mitigations visible.
Decision Making
Pre-Mortem
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
Purposedifferent
In early initiatives with many uncertainties, planning quickly turns too optimistic. A pre-mortem makes the expected failure visible in advance and sharpens the view of causes, gaps, and countermeasures.For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch.When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects.When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all.
Complexitydifferent
LowMediumHighLow
Timedifferent
20–45 min30-90 min1-4 Wochen1-5 Tage
Participantsdifferent
Small cross-functional group1-61-6Nutzertraffic
Formatdifferent
WorkshopWorkshop + asyncAsyncAsync
Outputdifferent
Risk list, Mitigation plan, Assumption logDecision Tree, Option Map, Assumption ListExperiment results, Decision log, Learning summaryInterest Metrics, Conversion Signal, Learning Note
Tagsno overlap
RiskDecisionFailurePlanning
DecisionTreeOptions
ExperimentsGrowthAnalyticsValidation
ValidationExperimentsDemandGrowth
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