Before collecting the data for a 2-variance test, a sample size calculation is used to obtain a power of 0.9. We want to measure the heart rate of a number of patients after administration of a drug in order to test whether the drug has an effect on the heart rate. We will take an example of a test on two variances. 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: XLSTAT includes several tests to compare variances and can also calculate the power or the number of observations required for a test based on Fisher's F distribution to compare variances. This tutorial explains how to calculate and interpret sample size and power to compare two variances in Excel using XLSTAT.
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