Sensitivity of normality tests to non-normal data

Nor Aishah Ahad, and Teh Sin Yin, and Abdul Rahman Othman, and Che Rohani Yaacob, (2011) Sensitivity of normality tests to non-normal data. Sains Malaysiana, 40 (6). pp. 637-641. ISSN 0126-6039


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In many statistical analyses, data need to be approximately normal or normally distributed. The Kolmogorov-Smirnov test, Anderson-Darling test, Cramer-von Mises test, and Shapiro-Wilk test are four statistical tests that are widely used for checking normality. One of the factors that influence these tests is the sample size. Given any test of normality mentioned, this study determined the sample sizes at which the tests would indicate that the data is not normal. The performance of the tests was evaluated under various spectrums of non-normal distributions and different sample sizes. The results showed that the Shapiro-Wilk test is the best normality test because this test rejects the null hypothesis of normality test at the smallest sample size compared to the other tests, for all levels of skewness and kurtosis of these distributions.

Item Type:Article
Keywords:monte carlo simulation; sample size; sensitivity; tests of normality
Journal:Sains Malaysiana
ID Code:2511
Deposited By: Mr Azam
Deposited On:03 Aug 2011 07:43
Last Modified:14 Dec 2016 06:31

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