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 Smoke Test |  Product Discovery Experiment Canvas |  Product Strategy DIBB |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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. | 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 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 | Low | Low | High |
Timedifferent | 1-5 Tage | 30-60 min | 1-2 h | 1-4 Wochen |
Participantsdifferent | Nutzertraffic | 1-5 | 2-8 | 1-6 |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Interest Metrics, Conversion Signal, Learning Note | Completed Experiment Canvas, Success Metric | DIBB document, Belief list, Bet list, Learning report | Experiment results, Decision log, Learning summary |
Tagsno overlap | ValidationExperimentsDemandGrowth | ExperimentsValidationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis | ExperimentsGrowthAnalyticsValidation |