Minitab displays the results of two hypothesis tests: the multiple comparisons test and Levene's test. Minitab calculates and displays a p-value for both tests:

·    If the p-value is larger than your chosen significance level (also called alpha or a), then you cannot reject the null hypothesis.

·    If the p-value is greater than a, then you can reject the null hypothesis and conclude that the variances (and the standard deviations) of the populations are different.

By default, Minitab's Test for Equal Variances command displays results for Levene's method and the multiple comparisons method. For most continuous distributions, both methods give you a type 1 error rate that is close to your specified significance level (also known as alpha or a. The multiple comparisons method is usually more powerful. If the p-value for the multiple comparisons method is significant, then you can use the summary plot to identify specific populations that have standard deviations that are different from each other.

In general, you should base your conclusions on the multiple comparisons test and corresponding multiple comparison intervals, unless you have small samples from very skewed, or heavy-tailed distributions. (See Tests of equal variance for further discussion.)

The high p-values for the tests (0.943 and 0.830) indicate that there is no difference between the variances (and the standard deviations) of the populations. This is also clear from the summary plot. However, with only 3 observations in each group, the tests do not have much power to detect a difference if one does exist. You should consider collecting more data to increase the power of the test.