Structure
The Texas sharpshooter fallacy often arises when a person has a large amount of data at their disposal, but only focuses on a small subset of that data. Random chance may give all the elements in that subset some kind of common property (or pair of common properties, when arguing for correlation). If the person fails to account for the likelihood of finding some subset in the large data with some common property strictly by chance alone, that person is likely committing a Texas Sharpshooter fallacy.
To illustrate, if we pay attention to a cluster of cancer cases in a certain sub-population and then draw our "circle" around the smallest area that includes this cluster, this sample will appear to be suffering an unusually high rate of cancer, but if we included the rest of the population, the incidence would regress to the average.
The fallacy is characterized by a lack of specific hypothesis prior to the gathering of data, or the formulation of a hypothesis only after data has already been gathered and examined. Thus, it typically does not apply if one had an ex ante, or prior, expectation of the particular relationship in question before examining the data. For example one might, prior to examining the information, have in mind a specific physical mechanism implying the particular relationship. One could then use the information to give support or cast doubt on the presence of that mechanism. Alternatively, if additional information can be generated using the same process as the original information, one can use the original information to construct a hypothesis, and then test the hypothesis on the new data. See hypothesis testing. What one cannot do is use the same information to construct and test the same hypothesis (see hypotheses suggested by the data) — to do so would be to commit the Texas sharpshooter fallacy.
Read more about this topic: Texas Sharpshooter Fallacy
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