Power, by definition, is the ability to find a statistically significant difference when the null hypothesis is in fact false, in other words power is your ability to find a difference when a real difference exists. The power of a study is determined by three factors: the sample size, the alpha level, and the effect size.
Most of the time when a researcher is concerned about issues regarding power it is when a study if first being proposed prior to collection of any data. In this situation, the investigator wants to determine what an appropriate sample size would be or justify a proposed sample size. In order to answer this question, the researcher needs to know the other two parts of the equation: alpha level and effect size. Determining an alpha level is usually a pretty easy task, figuring out the effect size is another matter.
Cohen, regarded as the deity of power analysis, (1977, 1988) justifies these levels of effect sizes.
|Effect size Index||Small||Medium||Large|
|t-test on Means||d||0.20||0.50||0.80|
|t-test on Correlations||r||0.10||0.30||0.50|
There are a number of web resources related to statistical power analyses