First, consider the ratio in the sample variances or the sample standard deviations, and then examine the confidence interval.
The estimated ratio of standard deviations and variances of your sample data is an estimate of the ratio in population standard deviations and variances. Because the estimated ratio is based on sample data and not on the entire population, it is unlikely that the sample ratio equals the population ratio. To better estimate the ratio, use the confidence interval.
The confidence interval provides a range of likely values for the ratio between two population variances or standard deviations. For example, a 95% confidence level indicates that if you take 100 random samples from the population, you could expect approximately 95 of the samples to produce intervals that contain the population ratio. The confidence interval helps you assess the practical significance of your results. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. If the interval is too wide to be useful, consider increasing your sample size. For more information, go to Ways to get a more precise confidence interval.
By default, the 2 variances test displays the results for Levene's method and Bonett's method. Bonett's method is usually more reliable than Levene's method. However, for extremely skewed and heavy tailed distributions, Levene's method is usually more reliable than Bonett's method. Use the Ftest only if you are certain that the data follow a normal distribution. Any small deviation from normality can greatly affect the Ftest results. For more information, go to Should I use Bonett's method or Levene's method for a 2 variance test?
The summary plot shows the confidence interval for the ratio and the confidence interval for either the standard deviations or variances.
 

In these results, the estimate for the population ratio of standard deviations for ratings from two hospitals is approximately 0.66. Using Bonett's method, you can be 95% confident that the population ratio of the standard deviations for the hospital ratings is between approximately 0.37 and 1.21.
For more information, go to Should I use Bonett's method or Levene's method for 2 Variances?
 
 

In these results, the null hypothesis states that the ratio in the standard deviations of ratings between two hospitals is 1. Because both pvalues are greater than the significance level of 0.05, you fail to reject the null hypothesis and cannot conclude that the standard deviations of the ratings between the hospitals are different.
Problems with your data, such as skewness and outliers can adversely affect your results. Use the graphs to look for skewness (by examining the spread of each sample) and to identify potential outliers.
When data are skewed, the majority of the data are located on the high or low side of the graph. Often, skewness is easiest to detect with a histogram or boxplot.
Data that are severely skewed can affect the validity of the pvalue if your sample is small (less than 20 values). If your data are severely skewed and you have a small sample, consider increasing your sample size.
Outliers, which are data values that are far away from other data values, can strongly affect the results of your analysis. Often, outliers are easiest to identify on a boxplot.
Try to identify the cause of any outliers. Correct any data–entry errors or measurement errors. Consider removing data values for abnormal, onetime events (also called special causes). Then, repeat the analysis. For more information, go to Identifying outliers.