One-Sample Equivalence Test
Power and Sample Size
Power Analysis - Power
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One-Sample Equivalence TestPower and Sample Size |
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For a 1-sample equivalence test, power is the ability to establish that the test mean is equivalent to your target value. In other words, power is the probability that you will conclude that the difference between the test mean and the target value is within your equivalence limits, when this is in fact true. See Power for equivalence tests for further discussion.
If you specify the sample size and the difference that you want to accommodate and still be able to claim equivalence, Minitab calculates the power for the test.
Example Output |
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1-Sample Equivalence Test
Power for difference: Test mean - target Null hypothesis: Difference ≤ -0.42 or Difference ≥ 0.42 Alternative hypothesis: -0.42 < Difference < 0.42 α level: 0.05 Assumed standard deviation: 0.732
Sample Difference Size Power 0.0 28 0.811090 0.1 28 0.707180 0.2 28 0.459102 0.3 28 0.211823 |
Interpretation |
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The snack bag analysis shows that, if the difference is larger, then you have less power to claim equivalence. If the difference is 0, then the power for the test with a sample size of 28 is approximately 0.81. If the difference is as much as 0.3, then the power is approximately 0.21.
With a power of 0.81, the chance that you will conclude that the difference is within your equivalence limits, when this is in fact true, is only 81%. Typically, you want a power of 0.9 or more.
The power curve is a useful way to visualize the relationship between power and the difference.
