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Selected:A/B Testing ARID (Active Reviews for Intermediate Designs)Causal Factor Analysis Assumption Mapping

Criterion	Growth A/B Testing	Architecture ARID (Active Reviews for Intermediate Designs)	Operations Causal Factor Analysis	Product Discovery Assumption Mapping
Purposedifferent	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.	ARID examines how well developers can work with a technical design. It focuses on a part of the system that can still be revised, between the architecture overview and the detailed specification.	For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events.	When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first.
Complexitydifferent	High	Medium	High	Medium
Timedifferent	1-4 Wochen	2–4 h review as a planning range; preparation and evaluation additional	2-6 h	45-60 min
Participantsdifferent	1-6	Designers, developers as users, and facilitation	3-10	2-8
Formatdifferent	Async	Workshop + async	Workshop + async	Workshop + async
Outputdifferent	Experiment results, Decision log, Learning summary	Prioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisions	Event Timeline, Causal Factor Chart, Cause List, Corrective Actions	Assumption map, Test backlog, Risk ranking
Tagsno overlap	ExperimentsGrowthAnalyticsValidation	ArchitectureEngineeringFeedbackValidationTasks	CausalityIncidentRoot causeTimeline	AssumptionsRiskExperimentsValidation

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