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Introduction to the power example
Using StatMate is entirely self-explanatory, and this example discusses the logic behind power analysis more than the mechanics of using StatMate.
We will continue analyzing the experiment discussed in the sample size example (Clinical Science 64:265-272, 1983). Now we'll use power analysis to interpret the results.
We determined the number of alpha2-adrenergic receptors on platelets of people with and without hypertension.
Here are the results:
|
Controls |
Hypertensives |
| Number of subjects |
17 |
18 |
| Mean receptor number (receptors/platelet) |
263 |
257 |
| Standard Deviation |
87 |
59 |
The data were analyzed with an unpaired t test. Here are the results from Prism:
Because the mean receptor number was almost the same in the two groups, the P value is very high. These data provide no evidence that the mean receptor number differs in the two groups.
While it is tempting to just stop with the conclusion that the results are "not statistically significant" (as we did in this study published 20 years ago), there are two ways to go further. One approach is to interpret the confidence interval. But here we'll use power analysis to evaluate the experiment.
Start StatMate.
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