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Criterion
Paper illustration for Planning Poker.
Agile
Planning Poker
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Purposedifferent
When estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions.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.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 knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.
Complexitydifferent
LowMediumLowHigh
Timedifferent
2-5 min je Item30-90 min1-2 h1-4 Wochen
Participantsdifferent
3-93-122-86-30 Experten
Formatdifferent
Workshop + asyncWorkshopWorkshop + asyncAsync
Outputdifferent
Relative Estimates, Assumption Notes, Split CandidatesAffinity Size Map, Grouped Estimates, Unclear ItemsDIBB document, Belief list, Bet list, Learning reportExpert Forecast, Consensus Range, Assumption Notes
Tagsno overlap
EstimationAgileRelative sizingTeam
EstimationBacklogRelative sizing
StrategyDecisionAssumptionsHypothesis
ForecastingExpertsDecisionStrategy
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