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

Suppose the researchers at the health management firm want to know how much power they will have if they sample 10 former patients each from hospitals A and B. They consider a difference of 10 points to be meaningful.

The results indicate that the test has a power value of 0.375063. This means that if the difference between population means is 10, there is only a 37.5063% chance that the test will detect it.

Such ambiguous results may not be worth the effort of conducting the test. Obviously, it would be desirable to increase the power of the test so that they can have more confidence in the results.