View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Growth A/B Testing |  Growth Funnel Analysis |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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 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 many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | Medium | High | Medium | Medium |
Timedifferent | 60-90 min | 1-4 Wochen | 1-3 h | 1-5 Tage |
Participantsdifferent | 3-8 | 1-6 | 1-5 | Nutzertraffic |
Formatdifferent | Workshop | Async | Async | Async |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | ValidationExperimentsDemandDiscovery |