The power of a hypothesis test is the probability that the test correctly rejects the null hypothesis. The power of a hypothesis test is affected by the sample size, the difference, the variability of the data, and the significance level of the test.
For more information, go to What is power?.
Minitab calculates the power of the test based on the specified ratio and sample size. A power value of 0.9 is usually considered adequate. A value of 0.9 indicates you have a 90% chance of detecting a difference between the population comparison standard deviation or variance and the hypothesized standard deviation or variance when a difference actually exists. If a test has low power, you might fail to detect a difference and mistakenly conclude that none exists. Usually, when the sample size is smaller or the ratio is closer to 1, the test has less power to detect a difference.
If you enter a ratio and a power value for the test, then Minitab calculates how large your sample must be. Minitab also calculates the actual power of the test for that sample size. Because sample sizes are whole numbers, the actual power of the test might be slightly greater than the power value that you specify.
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.