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Power
The power of a statistical test is the probability that the selected test will appropriately reject the null hypothesis (i.e., when an alternative hypothesis is true). That is, it refers to the likelihood that the test will not make a Type II error (false negative rate or β). Because power is equal to 1–β, as Type II error decreases, power increases. Statistical power is influenced by statistical significance, effect size, and sample size. All of these factors are taken into consideration when completing a power analysis.
Primary Factors That Influence Power
Several factors influence power, or the ability to detect significant results if they exist: statistical significance, effect size, and sample size. Each of these terms is described in this section.
Statistical Significance
The significance level (α) is the ...
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- “Coefficient Alpha and the Internal Structure of Tests”
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