To increase power:
- Increase alpha
- Conduct a one-tailed test
- Increase the effect size
- Decrease random error
- Increase sample size
Sound so simple, right? The reality is that although these 5 ways all work theoretically, you might have trouble with some in practice.
1. Alpha is pretty well set at .05 for most scientific studies (there are rare exceptions). So you’re not going to get away with this one.
2. One-tailed tests have nothing wrong with them theoretically. However, there are only a few tests in which they’re even possible–namely t- and z-tests. So they’re not used much and have therefore appear dubious. Most reviewers won’t believe you that you really were hypothesizing that direction (even if it’s obvious). They will assume you’re trying to artificially get that p-value lower (it has been done).
So, once again, unless you’re in an enlightened field, or one in which one sided tests are commonly done, you can forget this one too.
3. Unless you’re coming up with a more precise way to measure your constructs, it’s likely that the effect size is a big as it’s going to get. Keep going.
4. Aha, something we can work with. There are two great ways to reduce random error. One is to make it not random. Explain it with a control variable, turning into explained variation.
The other related way is to use some sort of repeated measures design. Because we have multiple measurements on a subject, we can now separate the error variance from the subject variance.
5. Finally, the crux of the matter. If #4 doesn’t work, and it won’t always, your only opton is to increase sample size. (But you knew that one, right?)
If you’d like to learn more about power and sample size estimates, take a look at our online workshop: Calculating Power and Sample Size. We’ll go over the logic, the info you need, where to get it, how to do the steps, and how to use power software to get good estimates. We’ll also go over what these estimates really tell you, and what they don’t.