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Normality and independent samples t-test

Assess normality when comparing two independent groups on a continuous outcome

The assumption of normality is the first statistical assumption that needs to be tested when comparing two independent groups on a continuous outcome. Normality of a continuous distribution is assessed using skewness and kurtosis statistics. In order to meet the statistical assumption of normality, both skewness and kurtosis statistics should be below an absolute value of 2.0. If either a skewness or a kurtosis statistic is above an absolute value of 2.0, then the continuous distribution is assumed to be non-normal. Independent samples t-tests should not be conducted on continuous variables that violate the assumption of normality. Independent samples t-tests should only be conducted on continuous outcomes that are normally distributed.

The steps for checking the assumption of normality for independent samples t-test in SPSS

1. Click Analyze.

2. Drag the mouse pointer over the Descriptive Statistics drop-down menu.

3. Select Descriptives.

4. Click on the continuous outcome variable to highlight it.

5. Click on the arrow to move the outcome variable into the Variable(s): box.

6. Click the Options tab.

7. Deselect Minimum and Maximum boxes under the Dispersion section.

8. Select the Kurtosis and Skewness boxes under the Distribution section.

9. Click Continue.

10. Select the Save standardized values as variables box.

11. Click OK.

The steps for interpreting the SPSS output for normality and independent samples t-test

1. Under the skewness and kurtosis columns of the Descriptive Statistics table, if the Statistic is less than an absolute value of 2.0, then researchers can assume normality. 

Was the assumption of normality met for independent samples t-test?

Yes
No

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