In the previous version, missing values in the grouping range will be considered as a group. Then go to the relevant instructions (S1, S2, S3, S4, S5, S6, S7, S8, S9 or S10) to perform the test in SPSS. The missing values in the data range will be excluded in the analysisįrom Origin 2015, missing values in the grouping range and the corresponding data values will be excluded in analysis. Supports limited sample size (10 ≤ n ≤ 2000). Especially effective for “non-normal” values.Įxtends Shapiro-Wilk test without loss of power. Best for symmetrical distributions with small sample sizes.Ĭan give better results for some datasets than Kolmogorov-Smirnov.īased on transformations of sample kurtosis and skewness. Kolmogorov-Smirnov test with corrected P. Here is the SPSS syntax for performing post hoc pairwise tests. Please look at the simple rule of selecting methods in table below.įor more details, please refer to the Choosing Normality Tests and Interpreting Results chapterĬommon normality test, but does not work well with duplicated data or large sample sizes.įor testing Gaussian distributions with specific mean and variance. such as normality, equality of variances and independence as quaint tools. Six different normality tests are available in Origin. If the assumption of normality is not valid, the results of the tests will be unreliable. Tests for normality are particularly important in process capability analysis because the commonly used capability indices are difficult to interpret unless. A number of statistical tests, such as the Student's t-test and the one-way and two-way ANOVA require a normally distributed sample population. These tests, which are summarized in the table labeled Tests for Normality, include the following: Shapiro-Wilk test. A normality test is used to determine whether sample data has been drawn from a normally distributed population (within some tolerance).