The Texas sharpshooter fallacy is the statistical fallacy of inferring meaning from what is essentially a random distribution of data points, functioning as the philosophical or rhetorical application of the multiple comparisons problem, also known in statistics as data dredging or p-hacking, together with apophenia, a concept from cognitive psychology. It is related to the clustering illusion, the tendency in human cognition to interpret patterns where none actually exist, a tendency that itself stems from an underlying tendency to underestimate how likely clusters are to appear by chance in a random or pseudorandom dataset. The name comes from an anecdote about a person in Texas who fires a gun at random into the side of a barn, producing a random distribution of bullet holes, and only afterward paints a target around the tightest cluster of shots to claim to be a sharpshooter.
Facts
Core ClaimThe Texas sharpshooter fallacy is the statistical fallacy of inferring meaning from what is essentially a random distribution of data points. 1 First Described Year Classification
Type of Phenomenon Connections
In Branch
Source Texas sharpshooter fallacy, Wikipedia
Sources
1. Texas sharpshooter fallacy, Wikipedia
Lead section, first sentence
The Texas sharpshooter fallacy is the statistical fallacy of inferring meaning from what is essentially a random distribution of data points.
Origin section
In the modern statistical literature, the story of a specifically Texas sharpshooter is first mentioned in 1977.
lead section, phenomenon-kind classification
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).
In Branch: Cognitive Psychology, Categories
Wikipedia article 'Texas sharpshooter fallacy' is filed under Category:Cognitive biases.
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