Step 1: Determine whether the test mean and the reference mean are equivalent
Compare the confidence interval with the equivalence limits. If the confidence interval is completely within the equivalence limits, you can claim that the mean of the test population is equivalent to the mean of the reference population. If part of the confidence interval is outside the equivalence limits, you cannot claim equivalence.
You can also use the p-values to evaluate the results of the equivalence test. To demonstrate equivalence, the p-values for both null hypotheses must be less than alpha.
Step 2: Check your data for problems
Problems with your data, such as skewness or outliers, can adversely affect your results. Use graphs to look for skewness (by examining the spread of the data) and to identify potential outliers.
Determine whether the data appear to be skewed
When data are skewed, the majority of the data is toward the high or low side of the graph. Often, skewness is easiest to identify with a boxplot or histogram.
Data that are severely skewed can affect the validity of the test results if your sample is small (< 20 values). If your data are severely skewed and you have a small sample, consider increasing your sample size.
Outliers, which are data points that are far away from most of the other data, can strongly affect your results. Outliers are easiest to identify on a boxplot.
By default, the equivalence test does not assume that the variances for each group are equal. However, if you selected the Assume equal variances option for the test, compare the graphs for each group to ensure that the spread of the data is similar. If the spread differs substantially, you should not assume equal variances when you perform the test.
To formally check for equal variances, use the test for 2 Variances.