If the data are not normal, use non-parametric tests. The test statistics are shown in the third table. Normality tests based on Skewness and Kurtosis. While Skewness and Kurtosis quantify the amount of departure from normality, one would want to know if the departure is statistically significant. Checking normality for parametric tests in SPSS . If it is, the data are obviously non- normal. Hence, a test can be developed to determine if the value of b 2 is significantly different from 3. 3. This test checks the variable’s distribution against a perfect model of normality and tells you if the two distributions are different. 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. The Kolmogorov-Smirnov and Shapiro-Wilk tests can be used to test the hypothesis that the distribution is normal. 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. SPSS Statistics Output. Recall that for the normal distribution, the theoretical value of b 2 is 3. 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. You will now see that the output has been split into separate sections based on the combination of groups of the two independent variables. This video demonstrates conducting the Shapiro-Wilk normality test in SPSS and interpreting the results. 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. Data does not need to be perfectly normally distributed for the tests to be reliable. 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. 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: Just make sure that the box for “Normal” is checked under distribution. D’Agostino (1990) describes a normality test based on the kurtosis coefficient, b 2. If the data are normal, use parametric tests. You can reach this test by selecting Analyze > Nonparametric Tests > Legacy Dialogs > and clicking 1-sample KS test. 4. 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