![]() As we do not know yet the parameters of our samples, we will use the concept of effect size. The goal of this tutorial will be to find the right sample size in order to obtain a power of 0.9. We want to compare the height-weight correlation for men and women. The main application of power calculations is to estimate the number of observations necessary to properly conduct an experiment. The statistical power calculations are usually done before the experiment is conducted. ![]() For a given power, it also allows to calculate the sample size that is necessary to reach that power. XLSTAT calculates the power (and beta) when other parameters are known. We therefore wish to maximize the power of the test. The power of a test is calculated as 1-beta1−beta and represents the probability that we reject the null hypothesis when it is false. We cannot fix it upfront, but based on other parameters of the model we can try to minimize it. In fact, it represents the probability that one does not reject the null hypothesis when it is false. The type II error or beta is less studied but is of great importance. It is set a priori for each test and is 5%. It occurs when one rejects the null hypothesis when it is true. The null hypothesis H0 and the alternative hypothesis Ha. When testing a hypothesis using a statistical test, there are several decisions to take: This tutorial explains how to calculate the sample size and power for a comparison of correlations in Excel using XLSTAT.
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