Use the power curve to assess the appropriate sample size or power for your test.
The power curve represents every combination of power and ratio for each sample size when the significance level is held constant. Each symbol on the power curve represents a calculated value based on the values that you enter. For example, if you enter a sample size and a power value, Minitab calculates the corresponding ratio and displays the calculated value on the graph.
Examine the values on the curve to determine the ratio that can be detected at a certain power value 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 hypothesis test has low power, you might fail to detect a ratio that is practically significant. 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. If you decrease the size of the ratio that you want to detect, the power also decreases.
When you perform 1 Variance in Basic Statistics, Minitab displays output for both the chi-square method and the Bonett method. However, when you perform Power and
Sample Size for 1 Variance, Minitab uses only the chi-square method.
In this graph, the power curve for a sample size of 50 shows that the test has a power of approximately 0.54 for a ratio of 0.8. For a sample size of 100, the power curve shows that the test has a power of approximately 0.87 for a ratio of 0.8. If a power of 0.87 is adequate for your situation, you should collect a sample size of 100. If you need to detect a ratio smaller than 0.8, you will need to collect a larger sample.