The power of a hypothesis test is nothing more than 1 minus the probability of a Type II error. Basically the power of a test is the probability that we make the right decision when the null is not correct (i.e. …

To understand the trade-o between Type I error and Type II error, consider the following example. In our justice system, a person on trial is assumed to be innocent until proven guilty. So, we set the null …

To determine the sample size needed for a study for which the goal is to get a signi cant result from a test, set and the desired power, decide on an alternative value that is practically interesting, estimate …

Type II error, also known as a "false negative": the error of not rejecting a null hypothesis when the alternative hypothesis is the true state of nature. In other words, this is the error of failing to accept …

Computations of power are specific to research designs, and no single paradigm exists for power estimations. However, use of a t-test based power profile provides the researcher with some direction.

An investigator is applying for a NIH grant and wants to show to his/her reviewers that the proposed study has at least 80% statistical power to detect an association of 2.0 (i.e., OR=2.0) when the …

high power of detecting the smallest relevant treatment effect, if it exists. By convention, studies with < 80% power (β > 0.20) are considered to have excessively high risk of false-negative results due to …