The power curve plots the power of the test versus the maximum difference between the smallest mean and the largest mean.
Use the power curve to assess the appropriate sample size or power for your test.
The power curve represents every combination of power and the maximum difference for each sample size when the significant level and the standard deviation are held constant. Each symbol on the power curve represents a calculated value based on the values that you enter for two properties. For example, if you enter a sample size and a power value, Minitab calculates the corresponding maximum difference and displays the value on the graph.
Examine the values on the curve to determine the maximum difference that the test can detect at a specific power and sample size. A power value of 0.9 is usually considered adequate. However, some practitioners consider a power value of 0.8 to be adequate. If a one-way ANOVA has low power, you might fail to detect a difference between the smallest mean and the largest mean when one truly exists.
If you increase the sample size, the power of the test also increases. You want enough observations in your sample to achieve adequate power. But you don't want a sample size so large that you waste time and money on unnecessary sampling or detect unimportant differences to be statistically significant.
In this graph, each sample size has its own curve. The power curve for a sample size of 5 (in each group) shows that the test has a power of approximately 0.8 for a maximum difference of 4. The power curve for a sample size of 7 shows that the test has a power of approximately 0.95 for a maximum difference of 4. The power curve for a sample size of 9 shows that the test has a power approaching 1.0 for a maximum difference of 4. For each sample size curve, as the maximum difference increases, the power also increases.