



| Term | Description |
|---|---|
![]() | Sample mean for sequence i (for more information, go to Methods and formulas for common concepts used in Equivalence Test for a 2x2 Crossover Design) |
| ni | Number of participants in sequence i |
| Si | Sample standard deviation of for sequence i |
By default, Minitab uses the following formula to calculate the 100(1 – α)% confidence interval (CI) for equivalence:
CI = [min(C, Dl), max(C, Du)]
where:



If you select the option to use the 100(1 – 2α)% CI, then the CI is given by the following formula:
CI = [Dl, Du]
For a hypotheses of Test mean > reference mean or Test mean - reference mean > lower limit, the 100(1 – α)% lower bound is equal to DL.
For a hypothesis of Test mean < reference mean or Test mean - reference mean < upper limit, the 100(1 – α)% upper bound is equal to DU.| Term | Description |
|---|---|
| D | Difference between the test mean and the reference mean |
| SE | Standard error |
| δ1 | Lower equivalence limit |
| δ2 | Upper equivalence limit |
| v | Degrees of freedom |
| α | The significance level for the test (alpha) |
| t1-α, v | Upper 1 – α critical value for a t-distribution with v degrees of freedom |
, and let t2 be the t-value for the hypothesis,
, where
is the difference between the mean of the test population and the mean of the reference population. By default, the t-values are calculated as follows:


For a hypothesis of Test mean > reference mean, δ1 = 0.
For a hypothesis of Test mean < reference mean, δ 2 = 0.
| Term | Description |
|---|---|
| D | Difference between the sample test mean and the sample reference mean |
| SE | Standard error of the difference |
| δ1 | Lower equivalence limit |
| δ2 | Upper equivalence limit |
| H0 | P-Value |
|---|---|
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| Term | Description |
|---|---|
![]() | Unknown difference between the mean of the test population and the mean of the reference population |
| δ1 | Lower equivalence limit |
| δ2 | Upper equivalence limit |
| v | Degrees of freedom |
| T | t-distribution with v degrees of freedom |
| t1 | t-value for the hypothesis ![]() |
| t2 | t-value for the hypothesis ![]() |
For information on how the t-values are calculated, see the section on t-values.