SPSS Statistics Output. The Kolmogorov-Smirnov test is often to test the normality assumption required by many statistical tests such as ANOVA, the t-test and many others. This video demonstrates conducting the Shapiro-Wilk normality test in SPSS and interpreting the results. The following two tests let us do just that: The Omnibus K-squared test; The Jarque–Bera test; In both tests, we start with the following hypotheses: However, it is almost routinely overlooked that such tests are robust against a violation of this assumption if sample sizes are reasonable, say N ≥ 25. In parametric statistical analysis the requirements that must be met are data that are normally distributed. This test checks the variable’s distribution against a perfect model of normality and tells you if the two distributions are different. You will now see that the output has been split into separate sections based on the combination of groups of the two independent variables. Recall that for the normal distribution, the theoretical value of b 2 is 3. If the data are not normal, use non-parametric tests. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n < 300), but may be unreliable for large samples. You can reach this test by selecting Analyze > Nonparametric Tests > Legacy Dialogs > and clicking 1-sample KS test. The test statistics are shown in the third table. The hypotheses used in testing data normality are: Ho: The distribution of the data is normal Ha: The distribution of the data is not normal. Here two tests for normality are run. How to Shapiro Wilk Normality Test Using SPSS Interpretation | The basic principle that we must understand is that the normality test is useful to find out whether a research data is normally distributed or not normal. Just make sure that the box for “Normal” is checked under distribution. While Skewness and Kurtosis quantify the amount of departure from normality, one would want to know if the departure is statistically significant. 4. If it is, the data are obviously non- normal. Checking normality for parametric tests in SPSS . If the data are normal, use parametric tests. Normal distributions can be divided up into the same proportions by the standard deviations, so 95% of the area under the curve lies within roughly plus or minus two standard deviations of the mean; In this video Jarlath Quinn demonstrates how to use the functions within the explore command in SPSS Statistics to test for normality. 3. One of the assumptions for most parametric tests to be reliable is that the data is approximately normally distributed. Data does not need to be perfectly normally distributed for the tests to be reliable. The Kolmogorov-Smirnov and Shapiro-Wilk tests can be used to test the hypothesis that the distribution is normal. 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