Increasing the sample size increases the power of your test. (See Power for equivalence tests for further discussion.) You want enough observations in your sample to achieve adequate power, but not so many that you waste time and money on unnecessary sampling.

If you specify the power and the difference, Minitab displays a power curve that shows the sample size that is required to achieve the specified level of power. Each power curve shows the relationship between power and the difference for that sample size.

The contact lens analysis shows that, if the difference is 0, then you need 10 pairs of observations to achieve a power of 0.9. A sample size of 10 gives you a power of approximately 0.93.

If the difference is closer to your lower equivalence limit or your upper equivalence limit (-0.5 or 0.5), you need more observations to achieve the same power. For example, if the difference is 0.4, you need at least 153 pairs of observations to achieve a power of 0.9.

For any sample size, the power of the test decreases and approaches a as the difference approaches the lower equivalence limit or the upper equivalence limit.

The Session window output provides additional details about the parameters for the analysis.