A Brief Review of Tests for Normality
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 1, January 2016, Pages: 5-12
Received: Dec. 24, 2015; Accepted: Jan. 5, 2016; Published: Jan. 27, 2016
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Keya Rani Das, Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
A. H. M. Rahmatullah Imon, Department of Mathematical Sciences, Ball State University, Muncie, IN, USA
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In statistics it is conventional to assume that the observations are normal. The entire statistical framework is grounded on this assumption and if this assumption is violated the inference breaks down. For this reason it is essential to check or test this assumption before any statistical analysis of data. In this paper we provide a brief review of commonly used tests for normality. We present both graphical and analytical tests here. Normality tests in regression and experimental design suffer from supernormality. We also address this issue in this paper and present some tests which can successfully handle this problem.
Power, Empirical Cdf, Outlier, Moments, Skewness, Kurtosis, Supernormality
To cite this article
Keya Rani Das, A. H. M. Rahmatullah Imon, A Brief Review of Tests for Normality, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 1, 2016, pp. 5-12. doi: 10.11648/j.ajtas.20160501.12
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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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