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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 5, Issue 1) |
| DOI | 10.11648/j.ajtas.20160501.12 |
| Page(s) | 5-12 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Power, Empirical Cdf, Outlier, Moments, Skewness, Kurtosis, Supernormality
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APA Style
Keya Rani Das, A. H. M. Rahmatullah Imon. (2016). A Brief Review of Tests for Normality. American Journal of Theoretical and Applied Statistics, 5(1), 5-12. https://doi.org/10.11648/j.ajtas.20160501.12
ACS Style
Keya Rani Das; A. H. M. Rahmatullah Imon. A Brief Review of Tests for Normality. Am. J. Theor. Appl. Stat. 2016, 5(1), 5-12. doi: 10.11648/j.ajtas.20160501.12
AMA Style
Keya Rani Das, A. H. M. Rahmatullah Imon. A Brief Review of Tests for Normality. Am J Theor Appl Stat. 2016;5(1):5-12. doi: 10.11648/j.ajtas.20160501.12
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Download@article{10.11648/j.ajtas.20160501.12,
author = {Keya Rani Das and A. H. M. Rahmatullah Imon},
title = {A Brief Review of Tests for Normality},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {5},
number = {1},
pages = {5-12},
doi = {10.11648/j.ajtas.20160501.12},
url = {https://doi.org/10.11648/j.ajtas.20160501.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160501.12},
abstract = {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.},
year = {2016}
}
TY - JOUR T1 - A Brief Review of Tests for Normality AU - Keya Rani Das AU - A. H. M. Rahmatullah Imon Y1 - 2016/01/27 PY - 2016 N1 - https://doi.org/10.11648/j.ajtas.20160501.12 DO - 10.11648/j.ajtas.20160501.12 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 5 EP - 12 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20160501.12 AB - 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. VL - 5 IS - 1 ER -
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