Statistical Methods for Data Science
The normality assumption is the assumption that the residuals (errors) of a statistical model are normally distributed. This assumption is crucial because many statistical methods, such as t-tests, z-tests, and regression analysis, rely on it to ensure valid results. When this assumption holds true, it enhances the reliability of hypothesis tests and confidence intervals, ultimately leading to more accurate conclusions from the data.
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