If this observed difference is adequately large, the test will reject the null hypothesis of population normality. Kolmogorov-Smirnov normality test This test compares the ECDF (empirical cumulative distribution function) of your sample data with the distribution expected if the data were normal. This test is similar to the Shapiro-Wilk normality test. Figure 8.11 Normality test dialog box Enterthe mmalty as variable to be tested for ' normality.
Practice while you learn with exercise files Download the files the instructor uses to teach the course. Minitab produces the output shown in Figure 8.12. The Ryan-Joiner statistic assesses the strength of this correlation if it is less than the appropriate critical value, you will reject the null hypothesis of population normality. Normality test using Minitab and beautiful graphs. If the correlation coefficient is near 1, the population is likely to be normal.
Ryan-Joiner normality test This test assesses normality by calculating the correlation between your data and the normal scores of your data. Mac: Statistics > Summary Statistics > Normality Test PC. If the observed difference is adequately large, you will reject the null hypothesis of population normality. Example of Normality Test Open the sample data, FatContent.MTW. To understand any P value, you need to know the null hypothesis. Anderson-Darling test This test compares the ECDF (empirical cumulative distribution function) of your sample data with the distribution expected if the data were normal. What question does the normality test answer The normality tests all report a P value. The test results indicate whether you should reject or fail to reject the null hypothesis that the data. The following are types of normality tests that you can use to assess normality. Choose Stat > Basic Statistics > Normality Test.