Use this tool to test if a sample is normally distributed. Description Assuming that a sample is normally distributed is common in statistical analysis. For example, in linear regression, in ANOVA or in ANCOVA the errors of the model are assumed to follow a normal distribution. As for any assumption, it is necessary, to test it.
XLSTAT offers you four methods to test the normality:
The Shapiro-Wilk test can be used with samples with 5000 and less observations.
The Anderson-Darling test proposed by Stephens (1974) is a modification of the Kolmogorov-Smirnov test and is suited to several distributions including the normal distribution for cases where the parameters of the distribution are not known and have to be estimated.
The Lilliefors test is a modification of the Kolmogorov-Smirnov test and is suited to normal cases where the parameters of the distribution, the mean and the variance are not known and have to be estimated.
The Jarque-Bera test is an asymptotic test which reliability increases with the number of observations.
This tool complements the "Distribution fitting" tool, which allows you to determine the value of the parameters of the normal distribution and to test the goodness of fit using a Chi-square or a Kolmogorov Smirnov test.