Abstract
Assessing the assumption of normality is required by most statistical procedures. Parametric statistical analysis is one of the best examples to show the importance of assessing the normality assumption. Parametric statistical analysis assumes a certain distribution of the data, such as the normal distribution. If the assumption of normality is violated, interpretation and inference may not be reliable or valid. Therefore, it is important to check for this assumption before proceeding with any relevant statistical procedures. There are significant amount of normality tests available in the literature. However, the most common normality test procedures available in statistical software are the Shapiro-Wilk (SW) test, Kolmogorov - Samirnov (KS) test, Anderson-Darling (AD) test, Cramer Von Mises (CVM) test and Pearson's chi-squared (PC) test. Some of these tests can only be applied under a certain condition or assumption. Moreover, different test of normality often produce different results .i.e. some tests reject while others fail to reject the null hypothesis of normality. The contradicting results are misleading and often confuse practitioners. Therefore, the choice of test of normality to be used should indisputably be given tremendous attention. This study focuseson comparing the power of five normality tests for the error term in Regression Analysis; SW, KS, AD, CVM and PC tests. The simulation process was carried out by using R programming language. The tests require different sample size to detect the non-normality assumption. As the sample size increases the power of these tests become close to each other. Keywords: Shapiro-Wilk (SW) Test, Kolmogorov -Samirnov (KS) Test, Cramer-von Mises (CVM) Test, Anderson-Darling (AD) Test, Pearson's Chi- squared (PC) Test.