Test statistic

Test (biology)

Chi-square test

Mathematics

Normality test

Biology

Statistical hypothesis testing

Z-test

Goldfeld–Quandt test

Statistics

Sample size determination

Paleontology

Anderson–Darling test

Multivariate statistics

Exact test

Univariate

Normality

Pearson's chi-squared test

Omnibus test

Kolmogorov–Smirnov test

Open Journal of Statistics

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.

Applications of Normality Test in Statistical Analysis