![]() ![]() (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.Ī Swedish study in 1992 tried to determine whether power lines caused some kind of poor health effects. ![]() 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. One could then use the information to give support or cast doubt on the presence of that mechanism. For example, one might, prior to examining the information, have in mind a specific physical mechanism implying the particular relationship. 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. The fallacy is characterized by a lack of a specific hypothesis prior to the gathering of data, or the formulation of a hypothesis only after data have already been gathered and examined. If the person attempts to account for the likelihood of finding some subset in the large data with some common property by a factor other than its actual cause, then that person is likely committing a Texas sharpshooter fallacy. Some factor other than the one attributed may give all the elements in that subset some kind of common property (or pair of common properties, when arguing for correlation). 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. In particular, random data points do not spread out but cluster, giving the impression of "hot spots" created by some underlying cause. Examining the points it is easy to identify apparent patterns. A set of 100 randomly generated coordinates displayed on a scatter graph. ![]()
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