A Texas sharpshooter fallacy occurs when someone draws conclusions based on only the consistent data – the data points that are similar to each other — ignoring data that may not support the conclusion. This does not allow the data to paint the full picture of what is really going on.
This fallacy gets its name from a story in which a Texas shooter fired many bullet holes into the side of a barn. He then drew a target around a tight cluster of bullet holes and called himself a “sharpshooter.” He wasn’t necessarily aiming anywhere in particular, but several of his bullets seemed to find a similar position in the barn wall. Drawing a target around the area made it look like he aimed, and succeeded in hitting, that particular area.
Let us say that after some digging, a researcher found out that during a time period that people were adopting a lot of pets, people were also going out to eat a lot. The researcher then hypothesized that going out to eat more often made people happier, which caused them to want to adopt a pet to love. However, the researcher failed to acknowledge that there were several other time periods when people were going out to eat a lot, but they were not adopting a lot of pets. This would show that the scientist’s hypothesis is incorrect, but he did not come to this conclusion this because he ignored all of the instances that did not support his hypothesis.
It’s very easy to focus on similarities in data. Humans naturally look for patterns because they makes things easier to understand. However, in order to avoid a Texas sharpshooter fallacy, it’s important to always look at the whole picture. Doing so may open your eyes to information you might not have been aware of if you had only focused on data that agreed with each other.
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