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| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy Counter Metrics |  Growth A/B Testing |
|---|---|---|---|
Purposedifferent | 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 a headline metric could mask side effects, it adds guardrail signals against unwanted outcomes. It connects customer value, product logic, and decision priorities. The result is captured as a counter-metric list and guardrail definitions. | 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 | Medium | Low | High |
Timedifferent | 60-90 min | 30-60 min | 1-4 Wochen |
Participantsdifferent | 3-8 | 2-6 | 1-6 |
Formatdifferent | Workshop | Workshop + async | Async |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | Counter Metric List, Guardrail Definitions | Experiment results, Decision log, Learning summary |
Tags1 shared | ExperimentsPrioritizationDiscoveryHypothesis | MetricsMeasurementStrategyExperiments | ExperimentsGrowthAnalyticsValidation |
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