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16 Φεβ 2021 · Statistical power: the likelihood that a test will detect an effect of a certain size if there is one, usually set at 80% or higher. Sample size: the minimum number of observations needed to observe an effect of a certain size with a given power level.
Statistical power, also called sensitivity, indicates the probability that a study can distinguish an actual effect from a chance occurrence. It represents the probability that a test correctly rejects the null hypothesis (i.e., it represents the probability of avoiding a Type I error).
Everything you want to know about power: relation to effect size, alpha, sample size & research design with calculation examples and software options.
Power in statistics is the probability that a hypothesis test can detect an effect in a sample when it exists in the population. It is the sensitivity of a hypothesis test. When an effect exists in the population, how likely is the test to detect it in your sample? You need the power!
The main application of statistical power is "power analysis", a calculation of power usually done before an experiment is conducted using data from pilot studies or a literature review.
The power of a hypothesis test is the probability of making the correct decision if the alternative hypothesis is true. That is, the power of a hypothesis test is the probability of rejecting the null hypothesis \(H_0\) when the alternative hypothesis \(H_A\) is the hypothesis that is true.
Let's take a look at another example that involves calculating the power of a hypothesis test. Let X denote the IQ of a randomly selected adult American. Assume, a bit unrealistically, that X is normally distributed with unknown mean μ and standard deviation 16.
