Goodness-of-fit Tests
Goodness-of-fit tests are statistical procedures used to determine how well a set of observed data fits a particular probability distribution. These tests are used to assess whether the observed data is significantly different from the expected data based on a particular theoretical distribution. The goodness-of-fit tests are used to evaluate the hypothesis that the observed data comes from a specific distribution. The most commonly used goodness-of-fit tests are the chi-square test, the Kolmogorov-Smirnov test, and the Anderson-Darling test. These tests are used in various fields, including finance, engineering, and social sciences, to determine the validity of a model or to compare different models. Goodness-of-fit tests are also used in machine learning to evaluate the performance of a model by comparing the predicted values with the actual values.
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