The Texas sharpshooter fallacy occurs when someone finds a pattern in random data after the fact and then presents it as if it were meaningful or predicted in advance, ignoring the data that doesn't fit the pattern.
The Texas Sharpshooter fallacy is a logical fallacy that occurs when someone finds a pattern or cluster in random or noisy data after the fact, and then presents it as if it were meaningful or predicted in advance. It is commonly used to describe situations where a person sifts through a large set of data, identifies a coincidental pattern, and then constructs a narrative or conclusion around that pattern — while ignoring all the data that doesn't fit.
The name comes from an analogy: imagine a person shooting randomly at the side of a barn, creating a scattered pattern of bullet holes. Afterward, the shooter paints a bulls-eye around the tightest cluster of holes, making it appear as though they were an expert marksman. This is analogous to the way someone can search through data, find a coincidental cluster, and then frame it as though it were a significant and predicted result.
The Texas Sharpshooter fallacy is related to confirmation bias, as both involve selectively focusing on evidence that supports a conclusion while ignoring contradictory evidence. However, the Texas Sharpshooter is more specific: it involves discovering a pattern post-hoc in random data and then treating it as if it were the target all along. This is particularly common in studies that measure many variables and then highlight only those that show a significant result, a practice sometimes called "data dredging" or "p-hacking."
The Texas Sharpshooter fallacy can be avoided by formulating hypotheses before examining the data, rather than constructing them afterward. It is important to consider all available data points, not just those that form a convenient pattern. When an interesting pattern is found in exploratory analysis, it should be tested with new, independent data before being treated as a meaningful finding. Additionally, transparency about how many variables were examined helps others evaluate whether a result is likely coincidental.
