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