ASSESSING NORMALITY TEST SELECTION AND VALIDATION: A COMPARATIVE APPROACH
Abstract
ABSTRACT
In statistics it is conventional to assume that the observations are normal. The entire statistical framework is grounded on this assumption and if 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 study we comparatively assessed some selected normality tests. Both graphical and numerical normality tests were considered and thereafter compared to check which one is better. Based on the analysis, conclusion was made that the Shapiro Wilk test is better for when sample size is small whereas Anderson Darling test is better for large sample and are therefore recommended for use in application.
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