Statistical Analysis of Strength of W/S Test of Normality Against Non-normal Distribution Using Monte Carlo Simulation
American Journal of Theoretical and Applied Statistics
Volume 6, Issue 5-1, September 2017, Pages: 62-65
Received: Feb. 24, 2017;
Accepted: Mar. 1, 2017;
Published: Jul. 11, 2017
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Ukponmwan H. Nosakhare, Department of General Studies, Mathematics and Computer Science Unit, Petroleum Training Institute, Warri, Nigeria
Ajibade F. Bright, Department of General Studies, Mathematics and Computer Science Unit, Petroleum Training Institute, Warri, Nigeria
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Among the test of normality in existence is the W/S which has standard table as the interval for critical region with both lower and upper bound. The test is suitable for sample size ranging from 3 as displayed in the W/S Critical table. But the sensitivity of the test can be determined by computation of power of the test which would show how sensitive the test is to non-normal distribution. The paper addressed the sensitivity of the test using some selected distributions which are from asymmetric and symmetric in nature. Monte Carlo Simulation technique was used with 100 replicates for sample sizes of 5 to 100 with regular interval of 5. Distributions considered include; Weibull, Chi-Square, t and Cauchy distributions. The result shows inconsistency of the test as it has weak power for distribution used except Cauchy distribution. The findings shows that the test should be used with caution has it has weak or low power which could lead to statistical error, thereby call for proper modification of the test to improve its power.
Range, Standard Deviation, Descriptive Statistics, Simulation, Power-of-Test
To cite this article
Ukponmwan H. Nosakhare,
Ajibade F. Bright,
Statistical Analysis of Strength of W/S Test of Normality Against Non-normal Distribution Using Monte Carlo Simulation, American Journal of Theoretical and Applied Statistics. Special Issue: Statistical Distributions and Modeling in Applied Mathematics.
Vol. 6, No. 5-1,
2017, pp. 62-65.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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