Texas Sharpshooter Fallacy (Multiple Comparisons Bias)

The Texas Sharpshooter Fallacy, also known as the multiple comparisons bias, occurs when conclusions are drawn from data that have been selectively chosen to confirm a hypothesis. The name comes from a shooter who fires bullets at a barn wall and then draws a target around the tightest cluster of holes, claiming a bullseye. This fallacy is common in statistical analysis, where researchers may test many hypotheses or look at many subgroups without correcting for multiple comparisons, leading to false positives. The classic example is a confidence trickster who sends different stock predictions to a large group, then only continues correspondence with those who received correct predictions. After several rounds, a small subset believes the predictions are accurate and invest money, even though the predictions were random. The key is that the sample is narrowed down to a group where chance alone produced the pattern, but the victims do not see the many incorrect predictions that were discarded.