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Criterion
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Paper illustration for NoEstimates.
Agile
NoEstimates
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
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 estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules.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
LowMediumMediumLow
Timedifferent
1-2 hlaufend1-2 Wochen pro Iteration30-60 min
Participantsdifferent
2-82-122-61-5
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
DIBB document, Belief list, Bet list, Learning reportThroughput Data, Flow Forecast, Slicing RulesPrioritized Assumption List, Test Plan, Results ReportCompleted Experiment Canvas, Success Metric
Tagsno overlap
StrategyDecisionAssumptionsHypothesis
EstimationForecastingFlow
ExperimentsValidationDiscoveryAssumptions
ExperimentsValidationDiscoveryHypothesis
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