![]() Screen shots will show how use this utility to calculate the Power for this example and also to provide a graph of Sample Size vs. A widely-used online Power calculation utility called G*Power is available for download from the Institute of Experimental Psychology at the University of Dusseldorf at this link: Fortunately there are a number of free utilities online that will readily calculate a test’s statistical Power. Manual calculation of a test’s Power given Alpha, Effect Size, Sample Size, and the Number of Tails are quite tedious. ![]() Typically alpha, Effect Size, and the Number of Tails are held constant while sample size is varied (usually increased) to achieve the desired Power for the statistical test. The four variables that are required in order to determine the Power for a one-sample t-Test are Alpha (α), Effect Size (d), Sample Size (n), and the Number of Tails. A Power of 0.8 indicates that a test has an 80 percent probability of detecting a difference. It is common to target a Power of 0.8 for statistical tests. Power is therefore a measure of the sensitivity of a statistical test. A false Negative represents a test not detecting a difference when a difference does exist.ġ – β = Power = the probability of a test detecting a difference when one exists. A Type 2 Error can be described as a False Negative. Β equals the probability of a Type 2 Error. Power is defined by the following formula: The Power of a t-Test is a measure of the test’s ability to detect a difference given the following parameters: Sign Test in Excel As A Paired t-Test Alternative Wilcoxon Signed-Rank Test in 8 Steps As a Paired t-Test Alternative Paired t-Test – Test Power With G-Power Utility Paired t-Test – Effect Size in Excel 2010, and Excel 2013 Paired t-Test Excel Calculations, Formulas, and Tools Paired t-Test in 4 Steps in Excel 2010 and Excel 2013Įxcel Normality Testing of Paired t-Test Data This is one of the following seven articles on Paired (2-Sample Dependent) t-Tests in Excel
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