The power of a test is its ability to detect an effect. It is always possible that, due to sampling error, a test will lead you to the wrong conclusion. Assessing power allows you to determine the probability that the test will correctly identify an effect if one exists.

If a test has low power, you may fail to detect an effect and mistakenly conclude that none exists. If the power of your test is too high, very small and possibly uninteresting effects can become significant.

If you provide the ratio that you want to be able to detect and the size of your samples, Minitab will calculate the power of the test.

For the lumber data, the manager wants to analyze the variability in length of lumber. He is interested in detecting a ratio of 0.75. He wants to know how powerful his test will be if he samples 50 pieces of wood.

The results indicate the test has a power value of 0.773867. Thus, if the ratio between the comparison and hypothesized standard deviations is 0.75, there is a 77.3867% chance that the test will detect this ratio.