Texas Sharpshooter Fallacy is cherry-picking data to fit a pattern after the fact. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0666, within the Reasoning family. The core principle: cherry-picking data to fit a pattern after the fact. 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
Cherry-picking data to fit a pattern after the fact.
Plain-English Definition
Cherry-picking data to fit a pattern after the fact.
Feynman Explanation
Draw the target around the bullet holes and call yourself a marksman.
Core Principle
Cherry-picking data to fit a pattern after the fact.
Mechanisms
Pending editorial review.
Cherry-picking data to fit a pattern after the fact.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Post-hoc pattern finding is not prediction.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Draw the target around the bullet holes and call yourself a marksman.
Examples
- Highlighting a strategy that worked in one region while ignoring the regions where it failed.
- Post-hoc pattern finding is not prediction.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
Most organizations meet this element as a personnel problem. It is not one. The mechanism underneath it is straightforward: cherry-picking data to fit a pattern after the fact. You can recognize it in the field by its signature: draw the target around the bullet holes and call yourself a marksman. 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, post-hoc pattern finding is not prediction. It is amplified whenever post-hoc pattern finding is not prediction. Inside organizations that shows up as post-hoc pattern finding is not prediction. 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 test patterns on new data and require pre-registration. 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
- Test patterns on new data and require pre-registration.
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.
- Test patterns on new data and require pre-registration.
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.
Using personal stories or isolated examples instead of evidence.
Concluding that a claim is false because the argument for it is flawed.
Assuming qualities of one thing transfer to another because they are associated.
Drawing conclusions about individuals from group-level data.
Assuming something exists because we can name or define it.
Relying solely on metrics that are easily quantified while ignoring what matters.
Deriving general rules from specific examples; the leap from instance to concept.
The brain evolved to reason adaptively, not always truthfully, to reduce the cost of errors.
We solve problems by adding, even when subtracting would be better.
Assuming that if one option is true, another must be false, when both can be true.
Presuming a purposeful actor behind events that may have no actor at all.
What cannot be settled by experiment is not worth debating.
Where Texas Sharpshooter Fallacy 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 Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
Questions about Texas Sharpshooter Fallacy
- What is Texas Sharpshooter Fallacy?
- Texas Sharpshooter Fallacy is cherry-picking data to fit a pattern after the fact. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0666, within the Reasoning family. The core principle: cherry-picking data to fit a pattern after the fact. 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 Texas Sharpshooter Fallacy?
- Post-hoc pattern finding is not prediction. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0666).
- How is Texas Sharpshooter Fallacy exploited?
- Post-hoc pattern finding is not prediction.
- How do you design around Texas Sharpshooter Fallacy?
- Test patterns on new data and require pre-registration.
- Which behavioral dimension does Texas Sharpshooter Fallacy belong to?
- Texas Sharpshooter Fallacy is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Reasoning", class "Mental Model". Its permanent identifier is HBT-COG-0666 and its evidence grade is B.