Statistical Power
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. The power of a statistical test is the probability that the test will reject a false null hypothesis (i.e. that it will not make a Type II error). As power increases, the chances of a Type II error decrease. The probability of a Type II error is referred to as the false negative rate ( ). Therefore power is equal to 1 . 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 confid...
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. The power of a statistical test is the probability that the test will reject a false null hypothesis (i.e. that it will not make a Type II error). As power increases, the chances of a Type II error decrease. The probability of a Type II error is referred to as the false negative rate ( ). Therefore power is equal to 1 . 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. 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. 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.
