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texas_sharpshooter
The Texas sharpshooter fallacy occurs when someone cherry-picks data clusters from a random set and then assigns significance to them after the fact. Named after a shooter who fires at a barn wall and then paints a target around the tightest cluster of bullet holes, it involves retrofitting a hypothesis to match observed data. It is a post hoc pattern-finding error that ignores the full data context.
"Look at this cancer cluster in the neighborhood near the factory. The factory must be causing cancer." (Ignoring that cancer clusters occur randomly in any large population, and this one was only noticed because it happened near a factory.)
A financial blogger posts: 'I recommended buying stocks in January, March, and November last year — and all three went up! My stock-picking method clearly works.' He quietly ignores the eight other picks that lost money.
A wellness influencer claims: 'Three of my followers who drank this herbal tea daily all reported fewer headaches within a month — proof that the tea cures headaches!' She ignores the hundreds of followers who reported no change.
Binary (yes/no) questions an LLM must answer to identify this aspect:
Was the hypothesis formed after observing the data pattern rather than before?
Type: binaryAre differences or non-fitting data points being ignored?
Type: binaryIs a causal or meaningful pattern being imposed on what may be random clustering?
Type: binaryThe Texas sharpshooter fallacy occurs when someone cherry-picks data clusters from a random set and then assigns significance to them after the fact. Named after a shooter who fires at a barn wall and then paints a target around the tightest cluster of bullet holes, it involves retrofitting a hypothesis to match observed data. It is a post hoc pattern-finding error that ignores the full data context.
Humans are compulsive pattern-seekers who find it nearly impossible to appreciate randomness. A cluster in data feels meaningful even when it is statistically expected by chance.
Ask whether the hypothesis was formed before or after seeing the data. Demand pre-registered predictions and consider the full dataset, not just the selected cluster.
Common in epidemiology scares, financial market 'technical analysis,' Bible code theories, and any field where patterns are sought in large datasets after the fact.
Use these tools to detect, analyze, or train this aspect.