Enter your data for Power and Sample Size for 1-Sample Equivalence Test

Stat > Power and Sample Size > Equivalence Tests > 1-Sample

From What do you want to determine? (Alternative hypothesis), select the alternative hypothesis that you are trying to prove or demonstrate.
- Lower limit < test mean - target < upper limit
  Test whether the difference between the mean of the test population and the target is within the limits that you specify.
  
  For example, an analyst wants to determine whether the mean strength of a generic drug is within ± 10 mg/ml of the target strength.
- Test mean > target
  Test whether the mean of the test population is greater than the target.
  
  For example, a food analyst wants to determine whether a less expensive formulation of a dry dog food has greater than 20 g of protein (per 100 g of food).
- Test mean < target
  Test whether the mean of the test population is less than the target.
  
  For example, an analyst wants to determine whether the mean time for a new medication to take effect is less than 5 minutes.
- Test mean - target > lower limit
  Test whether the difference between the mean of the test population and the target is greater than a lower limit.
  
  For example, a researcher wants to determine whether an experimental drug induces a mean reduction in diastolic blood pressure that is greater than the expected reduction (target) by 3 mm Hg or more.
- Test mean - target < upper limit
  Test whether the difference between the mean of the test population and the target is less than an upper limit.
  
  For example, an analyst wants to determine whether the mean waiting time in an emergency department is less than 10% over target.
Enter a value for each equivalence limit included in the alternative hypothesis.
- Lower limit
  Enter the lowest acceptable value for the difference. You want to demonstrate that the difference between the mean of the test population and the target is not lower than this value.
- Upper limit
  Enter the highest acceptable value for the difference. You want to demonstrate that the difference between the mean of the test population and the target does not exceed this value.
Specify values for two of the following power function variables. Leave the variable that you want to calculate blank.
Tip
If you enter multiple values into a field, separate the values with a space. You can also use shorthand notation to indicate multiple values. For example, you can enter 10:40/5 to indicate sample sizes from 10 to 40 in increments of 5.
- Sample sizes: Enter a sample size of interest. To assess the effect of different sample sizes, enter multiple values. Larger sample sizes give the test more power to demonstrate equivalence.
- Differences (within the limits): Enter one or more values to specify the difference between the population mean and the target value. The value of the difference must be within the equivalence limits. Differences that are close to an equivalence limit require larger sample sizes to achieve adequate power.
- Power values: Enter one or more values to specify the probability that the test shows equivalence when the population difference is within the equivalence limits. Common values are 0.8 and 0.9. For example, an analyst enters 0.9 to indicate a 90% chance that the test will demonstrate equivalence between the mean width of dowels and the target width when the mean and target are actually equivalent.
In Standard deviation, enter an estimate of the standard deviation. If you already collected and analyzed the data, you can use the standard deviation of the sample. If you do not have data, base your estimate on related research, design specifications, pilot studies, subject-matter knowledge, or similar information.