Texas Sharpshooter Fallacy
Version 1.0.0 · Updated 2026-07-28
CORE DEFINITION
The Texas sharpshooter fallacy is the statistical fallacy of inferring meaning from what is essentially a random distribution of data points. It is the philosophical or rhetorical application of the multiple comparisons problem (also known as data dredging or p-hacking in statistics) and apophenia (in cognitive psychology). It is related to the clustering illusion, which is the tendency in human cognition to see patterns in random data.
SCAFFOLDING EFFECT
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The Texas sharpshooter fallacy is the statistical fallacy of inferring meaning from what is essentially a random distribution of data points. It is the philosophical or rhetorical application of the multiple comparisons problem (also known as data dredging or p-hacking in statistics) and apophenia (in cognitive psychology). It is related to the clustering illusion, which is the tendency in human cognition to see patterns in random data.
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The sharpshooter first fires at a barn wall, creating a cluster of bullet holes, then draws a target around the densest cluster, claiming 'Look how accurate I am.' This metaphor illustrates the fallacy of first having data and then selecting a pattern, ignoring multiple comparisons, and treating random clustering as a significant pattern.
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- en.wikipedia.orghttps://en.wikipedia.org/wiki/Texas_sharpshooter_fallacyverified
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