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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration for OODA Loop.
Decision Making
OODA Loop
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
Purposedifferent
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conditions and separates real improvement from chance or expectation effects.In dynamic situations, decisions become outdated faster than they can be prepared. The OODA Loop holds observing, orienting, deciding, and acting together as a recurring rhythm so reaction does not slide into inertia.DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning 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
HighMediumLowLow
Timedifferent
1-4 Wochen15-60 min je Zyklus1-2 h30-60 min
Participantsdifferent
1-61-82-81-5
Formatdifferent
AsyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Experiment results, Decision log, Learning summarySituation Assessment, Decision Loop, Action UpdatesDIBB document, Belief list, Bet list, Learning reportCompleted Experiment Canvas, Success Metric
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
DecisionChangeLearningStrategy
StrategyDecisionAssumptionsHypothesis
ExperimentsValidationDiscoveryHypothesis
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