Type I vs. Type II Errors is false positives vs. false negatives. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0683, within the Probability family. The core principle: false positives vs. false negatives. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
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
Pending editorial review.
False positives vs. false negatives.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
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
- Hiring decisions, AI deployment, strategic pivots.
- Calibrating decision thresholds by which error is worse.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
Executives usually notice this element only after it has cost something. By then it looks like a one-off. It is not. The mechanism underneath it is straightforward: false positives vs. false negatives. You can recognize it in the field by its signature: both have a cost. Pick which one you'd rather make. Every element in the Cognition dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, calibrating decision thresholds by which error is worse. It is amplified whenever calibrating decision thresholds by which error is worse. Inside organizations that shows up as calibrating decision thresholds by which error is worse. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to explicitly state which error type is more costly before deciding. Measure the behavior, not the sentiment. A survey will tell you how people feel about this; only observed action tells you whether it changed.
Famous Experiments
Pending editorial review.
Design Principles
- Explicitly state which error type is more costly before deciding.
Measurement Approaches
Pending editorial review.
Evidence
Pending editorial review.
Pending editorial review.
The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Explicitly state which error type is more costly before deciding.
Pending editorial review.
Pending editorial review.
Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
Ignoring general statistics in favor of specific, vivid information.
Posterior = (likelihood × prior) / evidence.
Update beliefs in proportion to the strength of new evidence.
Start with a prior; update with new evidence.
Novices experiencing early success, often due to variance and small samples.
High-impact, hard-to-predict, retrospectively explainable events.
Striking pattern that is statistically expected in large samples.
Reasoning in distributions — ranges and probabilities — rather than points.
Average outcomes across the population differ from outcomes across time for one person.
A rough calculation using order-of-magnitude reasoning.
In quantum mechanics, certain pairs of properties cannot both be precisely known.
We ignore sample size when judging probability.
Where Type I vs. Type II Errors is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- EssayGoodhart's Law in the Real World
How measurable proxies capture judgment.
- EssayThe Perverse Incentives Hiding in Your KPIs
Cognitive shortcuts turned into scorecards.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Type I vs. Type II Errors
- What is Type I vs. Type II Errors?
- Type I vs. Type II Errors is false positives vs. false negatives. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0683, within the Probability family. The core principle: false positives vs. false negatives. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of Type I vs. Type II Errors?
- Calibrating decision thresholds by which error is worse. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0683).
- How is Type I vs. Type II Errors exploited?
- Calibrating decision thresholds by which error is worse.
- How do you design around Type I vs. Type II Errors?
- Explicitly state which error type is more costly before deciding.
- Which behavioral dimension does Type I vs. Type II Errors belong to?
- Type I vs. Type II Errors is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Probability", class "Mental Model". Its permanent identifier is HBT-COG-0683 and its evidence grade is B.