Parametric Test
In Data Science, a parametric test is a statistical hypothesis test that makes assumptions about the population parameters, such as the mean and variance, and uses these assumptions to make inferences about the population. These tests are used when the data is normally distributed and the sample size is large enough to apply the central limit theorem. Examples of parametric tests include t-tests, ANOVA, and linear regression. Parametric tests are often more powerful than non-parametric tests, which do not make assumptions about the population parameters, but they are also more sensitive to violations of these assumptions. Therefore, it is important to check the assumptions of normality and homogeneity of variance before conducting a parametric test.
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