Sensitivity of normality tests to non-normal data
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...
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Universiti Kebangsaan Malaysia
2011
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| Online Access: | http://journalarticle.ukm.my/2511/ http://journalarticle.ukm.my/2511/ http://journalarticle.ukm.my/2511/1/15_NorAishah.pdf |
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ukm-25112016-12-14T06:31:50Z http://journalarticle.ukm.my/2511/ Sensitivity of normality tests to non-normal data Nor Aishah Ahad, Teh Sin Yin, Abdul Rahman Othman, Che Rohani Yaacob, 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. Universiti Kebangsaan Malaysia 2011-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/2511/1/15_NorAishah.pdf 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 http://www.ukm.my/jsm/ |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Kebangasaan Malaysia |
| building |
UKM Institutional Repository |
| collection |
Online Access |
| language |
English |
| description |
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. |
| format |
Article |
| author |
Nor Aishah Ahad, Teh Sin Yin, Abdul Rahman Othman, Che Rohani Yaacob, |
| spellingShingle |
Nor Aishah Ahad, Teh Sin Yin, Abdul Rahman Othman, Che Rohani Yaacob, Sensitivity of normality tests to non-normal data |
| author_facet |
Nor Aishah Ahad, Teh Sin Yin, Abdul Rahman Othman, Che Rohani Yaacob, |
| author_sort |
Nor Aishah Ahad, |
| title |
Sensitivity of normality tests to non-normal data |
| title_short |
Sensitivity of normality tests to non-normal data |
| title_full |
Sensitivity of normality tests to non-normal data |
| title_fullStr |
Sensitivity of normality tests to non-normal data |
| title_full_unstemmed |
Sensitivity of normality tests to non-normal data |
| title_sort |
sensitivity of normality tests to non-normal data |
| publisher |
Universiti Kebangsaan Malaysia |
| publishDate |
2011 |
| url |
http://journalarticle.ukm.my/2511/ http://journalarticle.ukm.my/2511/ http://journalarticle.ukm.my/2511/1/15_NorAishah.pdf |
| first_indexed |
2023-09-18T19:36:17Z |
| last_indexed |
2023-09-18T19:36:17Z |
| _version_ |
1777405292324061184 |