View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | 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 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 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 | High | Medium |
Timedifferent | laufend | 30-90 min Setup, danach laufend | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 2-12 | 1-8 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | EstimationForecastingFlow | ForecastingFlowDelivery | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |