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| Criterion |  Product Discovery Experiment Canvas |  Growth Flywheel |  Product Discovery Hypothesis Prioritization Canvas |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | Growth often breaks down where a reinforcing mechanism builds up too much friction. A Flywheel shows the cycle of value, repetition, and reinforcement meant to sustain growth. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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. |
Complexitydifferent | Low | Medium | Medium | High |
Timedifferent | 30-60 min | 60-120 min | 60-90 min | 1-4 Wochen |
Participantsdifferent | 1-5 | 3-8 | 3-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop | Workshop | Async |
Outputdifferent | Completed Experiment Canvas, Success Metric | Flywheel Map, Friction Points, Growth Levers, Experiment Backlog | Prioritization Canvas, Hypothesis Backlog | Experiment results, Decision log, Learning summary |
Tagsno overlap | ExperimentsValidationDiscoveryHypothesis | GrowthRetentionConversion | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |