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 |
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 |
---|---|
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.