Coincidence is striking pattern that is statistically expected in large samples. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0158, within the Probability family. The core principle: striking pattern that is statistically expected in large samples. 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
Striking pattern that is statistically expected in large samples.
Plain-English Definition
Striking pattern that is statistically expected in large samples.
Feynman Explanation
In a big enough sample, the miraculous is mandatory.
Core Principle
Striking pattern that is statistically expected in large samples.
Mechanisms
Pending editorial review.
Striking pattern that is statistically expected in large samples.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Don't build strategy around 'too unlikely to be random' patterns.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
In a big enough sample, the miraculous is mandatory.
Examples
- Two competitors launching similar features the same week.
- Don't build strategy around 'too unlikely to be random' patterns.
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: striking pattern that is statistically expected in large samples. You can recognize it in the field by its signature: in a big enough sample, the miraculous is mandatory. 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, don't build strategy around 'too unlikely to be random' patterns. It is amplified whenever don't build strategy around 'too unlikely to be random' patterns. Inside organizations that shows up as don't build strategy around 'too unlikely to be random' patterns. 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 compute the actual probability before assigning meaning. 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
- Compute the actual probability before assigning meaning.
Measurement Approaches
Pending editorial review.
Evidence
Pending editorial review.
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Compute the actual probability before assigning meaning.
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.
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.
Bet size optimized to maximize long-run growth without ruin.
Where Coincidence 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 Coincidence
- What is Coincidence?
- Coincidence is striking pattern that is statistically expected in large samples. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0158, within the Probability family. The core principle: striking pattern that is statistically expected in large samples. 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 Coincidence?
- Don't build strategy around 'too unlikely to be random' patterns. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0158).
- How is Coincidence exploited?
- Don't build strategy around 'too unlikely to be random' patterns.
- How do you design around Coincidence?
- Compute the actual probability before assigning meaning.
- Which behavioral dimension does Coincidence belong to?
- Coincidence is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Probability", class "Mental Model". Its permanent identifier is HBT-COG-0158 and its evidence grade is B.