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What Is the Texas Sharpshooter Fallacy?
The Texas sharpshooter fallacy finds a pattern in random data and treats it as meaningful. Named after a shooter who fires at a barn and then paints a target around the tightest cluster of bullet holes, this fallacy selects data after the fact to create the appearance of a pattern.
Also known as Clustering Illusion, this is a informal fallacy — the error lies in the content or context of the argument rather than its formal structure.
How to Spot It
Example
Looking at a company's data and finding that sales peak on Tuesdays in March, then creating a theory about why March Tuesdays are special — when the pattern is random noise.
A Stronger Version
"We noticed sales seem higher on March Tuesdays in historical data. This could be random variation. Let's set a prediction: if March Tuesday sales exceed the average by 10% next year, the pattern may be real."
Warning Signs
- Patterns found after the fact in random data
- No prediction made before examining the data
- Cherry-picked time periods or subgroups
- Confusing noise with signal
How to Fix It
Patterns found after examining data need to be tested on new data. If you find a pattern, make a prediction and check whether it holds in the next data set. Patterns that only exist in retrospect may be random noise.
Paste your text above to scan for texas sharpshooter fallacy patterns and other reasoning errors. Each flagged passage includes an explanation and a suggestion for making the argument stronger.
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