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Criterion
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
A hypothetical failure makes concrete risks, signals, and fitting mitigations visible.
Decision Making
Pre-Mortem
Paper illustration for Dot Estimation.
Facilitation
Dot Estimation
Purposedifferent
For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch.When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes.In early initiatives with many uncertainties, planning quickly turns too optimistic. A pre-mortem makes the expected failure visible in advance and sharpens the view of causes, gaps, and countermeasures.When size or effort can only be estimated roughly, Dot Estimation condenses the group's experience into a quick range. It turns individual contributions into a visible selection. The result is captured as an Effort Heatmap, Risk Signals, and Discussion Targets.
Complexitydifferent
MediumMediumLowLow
Timedifferent
30-90 min30-90 min20–45 min5-20 min
Participantsdifferent
1-63-12Small cross-functional group3-20
Formatdifferent
Workshop + asyncWorkshopWorkshopWorkshop
Outputdifferent
Decision Tree, Option Map, Assumption ListAffinity Size Map, Grouped Estimates, Unclear ItemsRisk list, Mitigation plan, Assumption logEffort Heatmap, Risk Signals, Discussion Targets
Tagsno overlap
DecisionTreeOptions
EstimationBacklogRelative sizing
RiskDecisionFailurePlanning
EstimationEffortRisk
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