This case example involves the representation of empirical or statistical tests of normality using data of FDI inflows of India from 1994-2015. Case example of statistical tests of normality However, K-S Test can only be applied in SPSS. EViews and Stata support the Jarque-Bera test. Shapiro-Wilk test can be performed in SPSS and Stata. Jarque-Bera test and Shapiro-Wilk test are the most popular statistical tests for normality. * Best-suited for the sample between but can work till 5000. Test statistic value > critical Value Or P-Value < α value. Low power of the test for a finite sample. Not suitable for a heteroscedastic and autocorrelated sample. Identifies the probability of having data from normally distributed populationĭetermine the correlation between the observations.Ĭheck the joint probability of skewness and kurtosis from the normal distribution values. Kolmogorov-Smirnov Table yield conservative results. Not applicable for discrete distributions.Ĭentre values distribution is more sensitive. Modified to Lilliefors test for more accurate results. Helps in testing normality and goodness of fit. Information on the normally distributed data not required. Kolmogorov-Smirnov Goodness of Fit (K-S) Test.ĭerive the deviation of the cumulative frequency distribution of the variable with the expected normally distributed data. Statistical test of normality calculates the probability of deriving sampleįrom the normally distributed population. Statistical tests of checking normality of a dataset H a: Sample is derived from a normally distributed population. H 0: Sample is not derived from a normally distributed population. Typically represented by the below hypothesis. Thus, considering the characteristics of normally distributed data, a normality test needs to be performed for generating more effective results.