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Statistical Power. 12.1 The concept. The power of an experiment that you are about to carry out quanti es the chance that you will correctly reject the null hypothesis if some alternative hypothesis is really true. ctor experiment using ANOVA. We arbi-trarily choose = 0:05 (or some other val.
Statistical power, defined as 1-β, can be best described as the chance of finding a difference where there is one = the chance of rightly rejecting the null-hypothesis of no effect.
Design of Experiments is the area of statistics that examines plans on how to gather data to achieve good (or optimal) inference. Here, we will focus on the question of sample size:
1 Σεπ 2007 · We discuss the history of the concept of statistical power, the reasons for its ongoing neglect, its potential benefits to researchers, as well as actual ways to improve statistical power.
In designing a study to maximize the power of detecting a statistically significant comparison, it is generally better, if possible, to double the effect size θ than to double the sample size n, since standard errors of estimation decrease with the square root of the sample size.
3.1 Power calculations: quantitative data. Suppose you want to compare the mean in one group to the mean in another (i.e. carry out an unpaired t-test). The number, n, required in each group is given by. n = f(α, β) 2s2. ·.
In order to calculate power (or sample size), an investigator needs to have a question in mind, AND some difference (in means, rates, or median survival) in mind that would be meaningful to detect. Those differences might not be the same for every study.
