Insensitivity to Sample Size is we ignore sample size when judging probability. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0408, within the Probability family. The core principle: we ignore sample size when judging probability. 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
We ignore sample size when judging probability.
Plain-English Definition
We ignore sample size when judging probability.
Feynman Explanation
A five-star review from two customers is not the same as from two thousand.
Core Principle
We ignore sample size when judging probability.
Mechanisms
Pending editorial review.
We ignore sample size when judging probability.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Small samples produce noisy conclusions.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
A five-star review from two customers is not the same as from two thousand.
Examples
- A small pilot is treated as definitive proof.
- Small samples produce noisy conclusions.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
When this element shows up in a diagnostic, the instinct is to train people out of it. Training rarely moves it. The mechanism underneath it is straightforward: we ignore sample size when judging probability. You can recognize it in the field by its signature: a five-star review from two customers is not the same as from two thousand. 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, small samples produce noisy conclusions. It is amplified whenever small samples produce noisy conclusions. Inside organizations that shows up as small samples produce noisy conclusions. 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 require confidence intervals and sample-size reasoning. Design against it the way you would design against a known failure mode — assume it will appear, and price the exploit before someone finds it.
Famous Experiments
Pending editorial review.
Design Principles
- Require confidence intervals and sample-size reasoning.
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.
- Require confidence intervals and sample-size reasoning.
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.
Bet size optimized to maximize long-run growth without ruin.
Where Insensitivity to Sample Size 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 Insensitivity to Sample Size
- What is Insensitivity to Sample Size?
- Insensitivity to Sample Size is we ignore sample size when judging probability. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0408, within the Probability family. The core principle: we ignore sample size when judging probability. 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 Insensitivity to Sample Size?
- Small samples produce noisy conclusions. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0408).
- How is Insensitivity to Sample Size exploited?
- Small samples produce noisy conclusions.
- How do you design around Insensitivity to Sample Size?
- Require confidence intervals and sample-size reasoning.
- Which behavioral dimension does Insensitivity to Sample Size belong to?
- Insensitivity to Sample Size is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Probability", class "Mental Model". Its permanent identifier is HBT-COG-0408 and its evidence grade is B.