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Criterion
Paper illustration for Current Reality Tree
Systems Thinking
Current Reality Tree
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration of Riskiest Assumption Test with a method-specific labelled workspace.
Product Discovery
Riskiest Assumption Test
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
Purposedifferent
A Current Reality Tree condenses many symptoms into a few causal chains. The method makes visible which core problems drive several negative effects at once.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 an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report.When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric.
Complexitydifferent
HighHighMediumLow
Timedifferent
2-6 h1-4 Wochen1-2 Wochen pro Iteration30-60 min
Participantsdifferent
3-81-62-61-5
Formatdifferent
WorkshopAsyncWorkshop + asyncWorkshop + async
Outputdifferent
Current Reality Tree, Core Problems, Intervention IdeasExperiment results, Decision log, Learning summaryPrioritized Assumption List, Test Plan, Results ReportCompleted Experiment Canvas, Success Metric
Tagsno overlap
Systems thinkingRoot causeConstraintsCausality
ExperimentsGrowthAnalyticsValidation
ExperimentsValidationDiscoveryAssumptions
ExperimentsValidationDiscoveryHypothesis
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