Illusion of validity is a cognitive bias in which a person overestimates his or her ability to interpret and predict accurately the outcome when analyzing a set of data, in particular when the data analyzed show a very consistent pattern—that is, when the data "tell" a coherent story. This effect persists even when the person is aware of all the factors that limit the accuracy of his or her predictions, that is when the data and/or methods used to judge them lead to highly fallible predictions. Daniel Kahneman, Paul Slovic, and Amos Tversky explain the illusion as follows: "people often predict by selecting the output...that is most representative of the input....The confidence they have in their prediction depends primarily on the degree of representativeness...with little or no regard for the factors that limit predictive accuracy. Thus, people express great confidence in the prediction that a person is a librarian when given a description of his personality which matches the stereotype of librarians, even if the description is scanty, unreliable, or outdated. The unwarranted confidence which is produced by a good fit between the predicted outcome and the input information may be called the illusion of validity." In one study, for example, subjects reported higher confidence in a prediction of the final grade point average of a student after seeing a first-year record of consistent B's than a first-year record of an even number of A's and C's. Consistent patterns may be observed when input variables are highly redundant or correlated, which may increase subjective confidence. However, a number of highly correlated inputs should not increase confidence much more than only one of the inputs; instead higher confidence should be merited when a number of highly independent inputs show a consistent pattern.