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The Incentives Lab
HBT-COG-0683 · Dimension COG · Cognition

Type I vs. Type II Errors

False positives vs. false negatives.

Probability·Mental Model·Grade B·draft· enriching…

Scientific Definition

False positives vs. false negatives.

Plain-English Definition

False positives vs. false negatives.

Feynman Explanation

Both have a cost. Pick which one you'd rather make.

Core Principle

False positives vs. false negatives.

Mechanisms

Psychological

Pending editorial review.

Behavioral Economic

False positives vs. false negatives.

Neurological

Pending editorial review.

Evolutionary

Pending editorial review.

Sociological

Pending editorial review.

Computational

Pending editorial review.

Systems

Calibrating decision thresholds by which error is worse.

Inputs (Triggers)

Pending editorial review.

Outputs (Behaviors)

Pending editorial review.

Behavioral Signature

Both have a cost. Pick which one you'd rather make.

Examples

Everyday
  • Hiring decisions, AI deployment, strategic pivots.
Modern (Organizational)
  • Calibrating decision thresholds by which error is worse.
Historical

Pending editorial review.

Famous Experiments

Pending editorial review.

Design Principles

  • Explicitly state which error type is more costly before deciding.

Measurement Approaches

Pending editorial review.

Evidence

Evidence Grade
B (A strongest → E speculative)
Replication
★★★☆☆
Intervention Confidence
3 / 5
Consensus
Pending editorial review (HBT v1.0 auto-seed).
Limitations
Pending editorial review (HBT v1.0 auto-seed).
Open Research Questions

Pending editorial review.

Primary References

Pending editorial review.

Signature Section

The Perverse Incentive Lens™

How this behavior is exploited — and how to redesign around it.

Exploitation
Calibrating decision thresholds by which error is worse.
Amplifying Incentives
Calibrating decision thresholds by which error is worse.
Org Failure Modes
Calibrating decision thresholds by which error is worse.
Societal Failure Modes
Pending editorial review (HBT v1.0 auto-seed).
Ethical Considerations
Pending editorial review (HBT v1.0 auto-seed).
Redesign Strategies
Explicitly state which error type is more costly before deciding.
Diagnostic Questions
  • Explicitly state which error type is more costly before deciding.
Warning Signs

Pending editorial review.

Red Flags

Pending editorial review.

Intervention Playbook
Individual
Explicitly state which error type is more costly before deciding.
Team
Pending editorial review (HBT v1.0 auto-seed).
Organization
Pending editorial review (HBT v1.0 auto-seed).
Policy
Pending editorial review (HBT v1.0 auto-seed).
AI Implications
Detection
Pending editorial review (HBT v1.0 auto-seed).
Measurement
Pending editorial review (HBT v1.0 auto-seed).
Mitigation
Pending editorial review (HBT v1.0 auto-seed).
Responsible Use
Pending editorial review (HBT v1.0 auto-seed).

Interactive Mini Network

Click any neighbor to re-center the graph and follow the threads of connection.

HBT-COG-0683 · COG
Type I vs. Type II Errors
TIBRBase Rate FallacyBRBayes' Rule (Updating)BTBayes' TheoremBUBayesian UpdatingBLBeginner's LuckBSBlack SwanCoCoincidenceDiDistributionsErErgodicityFEFermi Estimate

Knowledge Graph Neighbors

Version History

v1.1.0 · 2026-06-28Initial auto-seed from corpus.