Hasty Generalization is sweeping conclusions from a small sample. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0354, within the Statistical Fallacy family. The core principle: sweeping conclusions from a small sample. 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
Sweeping conclusions from a small sample.
Plain-English Definition
Sweeping conclusions from a small sample.
Feynman Explanation
Five customers. One pattern. One billion-dollar decision.
Core Principle
Sweeping conclusions from a small sample.
Mechanisms
Sweeping conclusions from a small sample.
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Five customers. One pattern. One billion-dollar decision.
Examples
- Pilot of 5 succeeds — roll it out company-wide.
- Premature scaling of unrepresentative results.
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Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This element is common enough to feel like human nature and specific enough to be engineered around. The mechanism underneath it is straightforward: sweeping conclusions from a small sample. You can recognize it in the field by its signature: five customers. One pattern. One billion-dollar decision. 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, premature scaling of unrepresentative results. It is amplified whenever premature scaling of unrepresentative results. Inside organizations that shows up as premature scaling of unrepresentative results. 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 make the desired behavior observable, remove whatever currently pays for its opposite, and attach the reward to the behavior rather than to the noisy outcome downstream of it. Treat it as infrastructure. Once you can see it in your own system, most of the argument about culture resolves itself.
Famous Experiments
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Design Principles
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Measurement Approaches
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Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
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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.
A single story used as proof of a general claim.
n=1 generalized to n=everyone.
Quoting only the studies that support the position.
Selecting only the data that supports the conclusion.
Generalizing from winners while ignoring identical losers.
Drawing a target around wherever the bullets landed.
Attacking the person rather than the argument.
If A then B; B happened; therefore A.
It's true because authority says so.
Common sense says so, therefore it's true.
Believing something is true because of the consequences of its truth.
Substituting feeling for argument.
Where Hasty Generalization 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.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
Questions about Hasty Generalization
- What is Hasty Generalization?
- Hasty Generalization is sweeping conclusions from a small sample. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0354, within the Statistical Fallacy family. The core principle: sweeping conclusions from a small sample. 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 Hasty Generalization?
- Premature scaling of unrepresentative results. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0354).
- How is Hasty Generalization exploited?
- Premature scaling of unrepresentative results.
- How do you design around Hasty Generalization?
- Name the behavior you want in observable terms, remove what currently pays for the opposite, attach the reward to the behavior rather than a lagging proxy, and publish how you will detect gaming.
- Which behavioral dimension does Hasty Generalization belong to?
- Hasty Generalization is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Statistical Fallacy", class "Logical Fallacy". Its permanent identifier is HBT-COG-0354 and its evidence grade is B.