Applications of Normality Test in Statistical Analysis

In this study, to power comparison test, different univariate normality testing procedures are compared by using new algorithm. Different and multivariate test also analyzed here. And review efficient algorithm for calculating the size corrected of which can be used compare efficiency test. Also randomness generated random numbers. For purpose, 1000 data sets with combinations sample n = 10, 20, 25, 30, 40, 50, 100, 200, 300 were from uniform distribution tested tests randomness. The assessment statistical is sensitive size. Observed that increase n, overall powers increased but Shapiro Wilk (SW) Francia (SF) Andeson Darling (AD) most powerful among other tests. Cramer-Von-Mises (CVM) performs better than Pearson chi-square, Lilliefors has Jarque Bera (JB) Test. Test less