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 sample, Minitab will calculate the power of the test.

For the cooking oil data, the dietician suspects that the saturated fat content may be different from the advertised value of 15%. She is interested in detecting a difference of 1.5% or more. (She considers an effect smaller than this to be of little interest.) She wants to know how powerful her test will be if she samples 13 bottles.

The results indicate that the test has a power value of 0.547849. Thus, if the mean percentage of saturated fat for the population is 13.5% or 16.5% instead of 15%, there is only a 54.7849% chance that the test will detect this.

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 she can have more confidence in the results.