General Full Factorial
Power and Sample Size
Power Analysis - Power
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General Full FactorialPower and Sample Size |
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The power of a test is its ability to detect an effect if one exists. It is always possible that, due to sampling error, a test will lead you to the wrong conclusion. If a test has low power, you may fail to detect an effect and mistakenly conclude that none exists. If a test has high power, very small and possibly uninteresting effects can seem significant.
If you provide the maximum difference between means that you want to be able to detect and the number of replicates, Minitab will calculate the power of the test.
Example Output |
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General Full Factorial Design
α = 0.05 Assumed standard deviation = 0.15
Factors: 2 Number of levels: 3, 3
Include terms in the model up through order: 2 Not including blocks in model.
Maximum Total Difference Reps Runs Power 0.4 2 18 0.941635 |
Interpretation |
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The metal parts supplier wants to know how much power they will have if they use 2 replicates. They want to detect a maximum difference of 0.4.
The results indicate that the test has a power value of 0.941635. This means that if a change in the levels of one factor leads to a change of 0.4 in the population means, the test has a 94.1635% chance of detecting this effect.
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