statistical power
statistical powerThe power of a statistical test is the probability that the test will reject a false null hypothesis, or in other words that it will not make a Type II error. As power increases, the chances of a Type II error decrease, and vice versa. The probability of a Type II error is referred to as β. Therefore power is equal to 1 − β. Statistical tests attempt to use data from samples to determine if differences or similarities exist in a population. For example, to test the null hypothesis that the mean scores of men and women on a test do not differ, samples of men and women will be drawn, the test administered to them, and the mean score in each group compared with a statistical test. by William Trochim Identification of Misconceptions in Learning Statistical Power with Dynamic Graphics as a Remedial Tool. If the populations of men and women have different mean scores but the test of the sample data concludes that there is no such difference, a Type II error has been made. Statistical power depends on the significance criterion, the size of the difference or the strength of the similarity (that is, the effect size) in the population, and the sensitivity of the data. A significance criterion is a statement of how unlikely a difference must be, if the null hypothesis is true, to be considered significant. The most commonly used criteria are probabilities of 0. 05 (5%, 1 in 20), 0. 01 (1%, 1 in 100), and 0. Statistical power is the probability of getting a statistically significant |