Document Type: Framework
Status: Active
Version: v1.0
Authority: MWMS HeadOffice
Applies To: Experimentation Brain, Affiliate Brain, Finance Brain
Parent: Experimentation Brain
Last Reviewed: 2026-04-03
Purpose
This page defines how experiment signal strength is classified before decisions are made regarding capital allocation or scaling.
Signal strength classification ensures:
- evidence quality is understood
- weak signals are not over-interpreted
- strong signals are not ignored
- scaling decisions are justified
- learning discipline remains consistent
Signal strength must be interpreted consistently across all experiments.
Core Principle
Not all positive signals are strong signals.
Signal strength determines decision confidence.
Signal Strength Levels
Level 1 — Weak Signal
Characteristics:
early indications of behavioural response
limited data volume
inconsistent performance patterns
high noise level
unclear interpretation
Typical interpretation:
possible directional indication but insufficient confidence for exposure increase
Level 2 — Emerging Signal
Characteristics:
observable behavioural pattern
initial consistency across limited conditions
moderate noise level
hypothesis partially supported
Typical interpretation:
requires additional testing before exposure increase
Level 3 — Developing Signal
Characteristics:
repeatable behaviour across multiple variations
increasing consistency
noise reducing
hypothesis increasingly supported
Typical interpretation:
may justify movement to higher testing allocation band
Level 4 — Strong Signal
Characteristics:
stable behavioural response
consistent performance across variations
low noise environment
clear hypothesis support
Typical interpretation:
may justify preparation for scaling conditions
Level 5 — Validated Signal
Characteristics:
repeatable behaviour across multiple test environments
stable performance indicators
clear interpretability
multiple aligned indicators
high confidence level
Typical interpretation:
may justify scaling exposure consideration
Signal Strength Dimensions
Signal strength should consider:
behaviour consistency
repeatability
clarity of interpretation
noise level
hypothesis alignment
stability across variations
Signal Strength Discipline
Strong signal classification requires:
clear interpretation logic
consistent observed behaviour
reduced conflicting indicators
Signal strength should not be upgraded prematurely.
Signal Strength and Capital Relationship
Signal strength influences:
allocation band progression
capital velocity constraints
confidence thresholds
scaling readiness evaluation
Signal Strength Warning
Single metric improvement does not automatically indicate strong signal.
Signal strength should consider multi-dimensional indicators.
Relationship to Other Pages
Experimentation Capital Escalation Triggers
Experimentation De-escalation Signals
Finance Brain Capital Confidence Thresholds
Finance Experimentation Alignment Model
Experimentation Brain Canon
Architectural Role
This page defines the shared language used when interpreting experimental evidence strength.
Future Expansion
Future versions may include:
signal scoring frameworks
confidence weighting models
signal dashboards
automated signal classification
Change Log
Version: v1.0
Date: 2026-04-03
Author: MWMS HeadOffice
Change: Initial creation of Experimentation Signal Strength Classification defining structured interpretation of evidence strength.