power

(Statistical) Power (Hypothesis Testing) (GWU EMSE-271)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | Nomenclature | Concepts [confidence intervals | null hypothesis | acceptance and rejection terminology | Type I and Type II errors | power | p-value]

"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. 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]] (β). Therefore power is equal to 1 − β." - [|Wikipedia]


 * POWER ANALYSIS**

"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." - [|Wikipedia]

**More ==>** Calculating Sample Size (TBD) PLACEHOLDER


 * Sources:**
 * EMSE 271, Fall 2009
 * Statistical power. (2009, September 21). In //Wikipedia, The Free Encyclopedia//. Retrieved 13:36, September 21, 2009, from []