Normality tests are a fast way of determining if your data is non-normal or not. Testing for normality is often the first step in analyzing your data. Many statistical tools that you might us ...

Tests for normality are particularly important in process capability analysis because the commonly used capability indices are difficult to interpret unless the data are at least approximately ...

These tests often employ advanced metrics, such as weighted L2-statistics and comparisons via the moment generating function, to sensitively detect departures from normality.

The use of the plots usually centers on detecting irregular tail behavior or outliers. Along with the normal plot, we develop tests for various departures from normality, especially for skewness and ...

A Tests for Normality table includes the Shapiro-Wilk, Kolmogorov, Cramer-von Mises, and Anderson-Darling test statistics, with their corresponding p -values, as shown in Figure 38.14.