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Statistical tests use data from samples to assess, or make inferences about, a population. In addition, the concept of power is used to make comparisons between different statistical tests: for example, between a parametric and a nonparametric test of the same hypothesis. It can also be used to calculate the minimum effect size that is likely to be detected in a study using a given sample size. Power analysis can be used to calculate the minimum sample size required to accept the outcome of a statistical test with a particular level of confidence.
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The probability of a Type II error is referred to as the false negative rate (β). As power increases, the chances of a Type II error decrease. The power of a statistical test is the probability that the test will reject the null hypothesis when the alternative hypothesis is true (i.e.