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 Assumption Mapping |  Delivery Monte Carlo Forecasting |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. | 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 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 | High | Low | High |
Timedifferent | 45-60 min | 30-90 min Setup, danach laufend | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 2-8 | 1-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Assumption map, Test backlog, Risk ranking | Forecast Percentiles, Throughput Dataset, Risk Communication | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | AssumptionsRiskExperimentsValidation | ForecastingFlowDelivery | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |