Complete the following steps to interpret a test for equal
variances. Key results in the tables include the standard deviation, the 95%
Bonferroni confidence intervals, and the individual confidence level. Key
results on the summary plot include the multiple comparisons, p-values, and the
Step 1: Estimate the standard deviation of each population
Use the Bonferroni confidence intervals to estimate the standard deviation of each population based on your categorical factors. Each confidence interval is a range of likely values for the standard deviation of the corresponding population.
Minitab adjusts the Bonferroni confidence intervals to maintain the simultaneous confidence level. With 95% Bonferroni confidence intervals, you can be 95% confident that the entire set of confidence intervals includes the true population standard deviations for all groups.
Controlling the simultaneous confidence level is especially important when you perform multiple comparisons. If you do not control the simultaneous confidence level, the chance that at least one confidence interval does not contain the true standard deviation increases with the number of confidence intervals.
You cannot use the Bonferroni confidence intervals to determine whether the differences between pairs of groups are statistically significant. To determine the statistical significance of the differences between pairs of groups, use the multiple comparison intervals in step 2.
Step 2: Determine whether the population standard deviations are different
Use the summary plot to determine whether any of the differences between the standard deviations are statistically significant. The types of tests and intervals that Minitab displays depends on whether you checked Use test based on normal distribution in the Options sub-dialog box and on the number of groups in your data.
The summary plot displays the p-values for either one or two tests that assess the equality of variances. The summary plot also displays either multiple comparison intervals or Bonferroni confidence intervals.
P-values for the tests
Use the p-values on the summary plot to determine whether any of the differences between the standard deviations are statistically significant. Minitab displays the results of either one or two tests that assess the equality of variances. If you have two p-values and they disagree, go to All Statistics and graphs and click "Tests" for information about which test to use.
Compare the p-value to your significance level to assess the null hypothesis. The null hypothesis is that the group standard deviations are all equal. Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
Use the following guidelines to interpret the p-values:
If the p-value is > α, the differences between the standard deviations are not statistically significant.
If the p-value is ≤ α, the differences between some of the standard deviations are statistically significant.
Multiple comparison intervals
If you did not select Use test based on normal distribution, the summary plot displays confidence intervals for the multiple comparisons method. In general, you can base your conclusions on the multiple comparisons test and the multiple comparison intervals, unless you have small samples from very skewed or heavy-tailed distributions.
Use the multiple comparison intervals to identify specific pairs of standard deviations that are not equal. If two intervals do not overlap, the difference between the corresponding standard deviations is statistically significant. If the p-value for the multiple comparisons test is less than your significance level, at least one pair of intervals does not overlap.
Bonferroni confidence intervals
If you selected Use test based on normal distribution, the summary plot displays Bonferroni confidence intervals to estimate the standard deviation of each population. You cannot use these intervals to determine whether the differences between pairs of groups are statistically significant. Go to step 1 for more information about how to interpret Bonferroni confidence intervals.