The Texas Sharpshooter Fallacy is a logical fallacy based on the metaphor of a gunman shooting the side of a barn, then drawing targets around the bullet-hole clusters to make it look like he hit the target.
This logical fallacy, also called a clustering illusion, illustrates how people look for similarities, ignoring differences, and do not account for randomness.
Why is it called the Texas sharpshooter fallacy?
It gets its name from a story about a Texan man with no shooting skills who fired his gun at a barn wall and then proceeded to paint a target around the closest cluster of bullet holes. When the paint dried, he invited his neighbours to see what a great shot he was and he points at the bullet-riddled target as evidence of his expert marksmanship.
His neighbours were impressed and they thought it was very unlikely that the man could have hit every target in the bullseye unless he was an extraordinary marksman. They therefore declared the man to be the greatest sharpshooter in the state.
Those that rely on the Texas sharpshooter fallacy tend to cherry-pick data clusters based on a predetermined conclusion.
But here’s the thing – in every collection of data there will most likely be coincidental clusters. If you fire one hundred random gunshots then there will be clusters of bullet holes.
Human perception tends to identify patterns where none actually exist so instead of letting a full spectrum of evidence lead us to a logical conclusion, we find patterns and correlations in support of our goals, and ignore evidence that contradicts them or suggests the clusters weren’t actually statistically significant.
The fallacy outlines how we can ignore randomness when determining whether results are meaningful, focusing on similarities and ignoring differences.
But when it comes to science, for findings to be convincing, the bull’s-eye should be painted before firing the bullets (the target should be pre-specified).