Use 2-Sample Equivalence Test to evaluate whether the mean of a test population is equivalent to the mean of a reference population when you have two independent samples.
When you use a 2-sample equivalence test, you must specify a range of values that are "close enough" to be considered equivalent to the reference mean. This equivalence interval, also called the zone of equivalence, is based on your knowledge of the product or process and should be determined before you perform the test. The analysis then determines whether you have enough evidence to claim that the difference (or ratio) between the population means is within the equivalence interval.
For example, an analyst wants to test whether the strength of a generic drug is equivalent to the strength of a brand name drug. The analyst defines equivalence as a difference in mean strength of ±0.1 mg. If the confidence interval of the difference between the mean strength of the generic drug and the mean strength of the brand name drug is contained completely within the equivalence interval (–0.1, 0.1), then the mean strengths of the two drugs are equivalent.
You can also use the 2-sample equivalence test to perform superiority tests and inferiority tests, to evaluate whether the mean of a test population is greater than or less than the mean of a reference population.
To perform a 2-sample equivalence test, choose .
If you have paired (dependent) observations on the same person or item, use Equivalence Test with Paired Data. For more information, go to How are dependent and independent samples different?.
To prove that the two population means are not equal when you have two independent samples, use 2-Sample t.