Document Type: Framework
Status: Active
Version: v1.0
Authority: MWMS HeadOffice
Applies To: Experimentation Brain, Research Brain, Affiliate Brain, Finance Brain
Parent: Experimentation Brain
Last Reviewed: 2026-04-03
Purpose
This page defines the maturity levels of experimental evidence as it develops through structured testing.
Evidence maturity helps determine:
- when learning is still forming
- when signals become reliable
- when confidence is justified
- when capital exposure may increase
- when scaling readiness may be considered
Evidence matures as uncertainty decreases.
Core Principle
Early signals suggest direction.
Mature evidence supports decisions.
Evidence Maturity Levels
Level 1 — Preliminary Evidence
Characteristics:
limited data
early behavioural indicators
high noise level
interpretation uncertainty
weak hypothesis confirmation
Typical use:
directional insight only
Capital implication:
minimal exposure only
Level 2 — Forming Evidence
Characteristics:
observable behavioural patterns
early signal consistency
moderate noise level
hypothesis partially supported
Typical use:
structured testing refinement
Capital implication:
controlled exposure appropriate
Level 3 — Structured Evidence
Characteristics:
repeatable behavioural patterns
increasing signal clarity
reduced noise
multiple supporting indicators
Typical use:
Phase 4 structured testing
Capital implication:
managed exposure may be appropriate
Level 4 — Reliable Evidence
Characteristics:
consistent behavioural response
stable signal clarity
low volatility
hypothesis strongly supported
Typical use:
pre-scaling evaluation
Capital implication:
elevated exposure consideration possible
Level 5 — Mature Evidence
Characteristics:
repeatable results across multiple environments
strong interpretability clarity
stable performance indicators
multiple aligned signals
Typical use:
scaling readiness consideration
Capital implication:
strategic exposure consideration possible
Evidence Maturity Dimensions
Evidence maturity should consider:
signal repeatability
signal clarity
noise reduction
hypothesis validation strength
cross-test consistency
interpretation reliability
Evidence Maturity Discipline
Evidence maturity should not be assumed prematurely.
Maturity should reflect accumulated signal strength.
Evidence Maturity Regression
Evidence maturity may decrease when:
signal volatility increases
unexpected behaviour appears
hypothesis support weakens
interpretation clarity reduces
Relationship to Other Pages
Experimentation Signal Strength Classification
Experimentation Confidence Progression Model
Experimentation Evidence Validation Criteria
Experimentation Decision Progression Logic
Finance Brain Capital Confidence Thresholds
Architectural Role
This page defines the staged maturity of learning evidence as it develops through structured experimentation.
Future Expansion
Future versions may include:
evidence scoring models
maturity dashboards
confidence weighting systems
automated maturity classification
Change Log
Version: v1.0
Date: 2026-04-03
Author: MWMS HeadOffice
Change: Initial creation of Experimentation Evidence Maturity Levels defining structured development of experimental evidence.