Selection Bias is your sample isn't random. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0601, within the Probability family. The core principle: your sample isn't random. 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
Your sample isn't random.
Plain-English Definition
Your sample isn't random.
Feynman Explanation
Surveys of your best customers are not surveys of your customers.
Core Principle
Your sample isn't random.
Mechanisms
Pending editorial review.
Your sample isn't random.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Strategy decisions distorted by non-random data.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Surveys of your best customers are not surveys of your customers.
Examples
- Customer feedback dominated by power users.
- Strategy decisions distorted by non-random data.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This is one of the elements leaders describe as a values gap. It is a payoff gap. The mechanism underneath it is straightforward: your sample isn't random. You can recognize it in the field by its signature: surveys of your best customers are not surveys of your customers. 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, strategy decisions distorted by non-random data. It is amplified whenever strategy decisions distorted by non-random data. Inside organizations that shows up as strategy decisions distorted by non-random data. 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 always ask: 'Who's not in this data?'. The leverage is not in explaining the behavior to people. It is in changing what the behavior earns.
Famous Experiments
Pending editorial review.
Design Principles
- Always ask: 'Who's not in this data?'
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.
- Always ask: 'Who's not in this data?'
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.
We judge probability by absolute counts rather than ratios.
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.
Where Selection Bias 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.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- 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 Selection Bias
- What is Selection Bias?
- Selection Bias is your sample isn't random. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0601, within the Probability family. The core principle: your sample isn't random. 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 Selection Bias?
- Strategy decisions distorted by non-random data. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0601).
- How is Selection Bias exploited?
- Strategy decisions distorted by non-random data.
- How do you design around Selection Bias?
- Always ask: 'Who's not in this data?'
- Which behavioral dimension does Selection Bias belong to?
- Selection Bias is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Probability", class "Mental Model". Its permanent identifier is HBT-COG-0601 and its evidence grade is B.