![]() ![]() If one data set is used to construct a hypothesis, then a new data set must be generated (ideally, in a different way, based on predictions made by the hypothesis) to test it. A hypothesis must be constructed before data is collected based on that hypothesis. The Texas sharpshooter fallacy uses the same data to both construct and test a hypothesis. However, when the region of significance is determined after the event has occurred, any outcome at all can be made to appear spectacularly improbable. In normal target practice, the bullseye defines a region of significance, and there's a low probability of hitting it by firing in a random direction. Though the shot may have been totally random, he makes it appear as though he has performed a highly non-random act. The fallacy's name comes from a parable in which a Texan fires his gun at the side of a barn, paints a bullseye around the bullet hole, and claims to be a sharpshooter. The Texas sharpshooter fallacy (or clustering fallacy) occurs when the same data is used both to construct and test a hypothesis (4) 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. It is related to the clustering illusion, which is the tendency in human cognition to interpret patterns where none actually exist. From this reasoning, a false conclusion is inferred. The Texas sharpshooter fallacy is an informal fallacy which is committed when differences in data are ignored, but similarities are overemphasized. The name comes from a joke about a Texan who fires some gunshots at the side of a barn, then paints a target centered on the tightest cluster of hits and claims to be a sharpshooter. ![]() ![]() Please note in the comments if the question is too long and should be rephrased more concise. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |