We are receiving a lot of questions about relevant analyses in the Analyse-it Method Validation edition to help in evaluating new diagnostic tests in the fight against COVID-19. Below are some quick links that will help, but contact us if you have questions - we are working as normal.
Also see our latest blog post: Sensitivity/Specificity and The Importance of Predictive Values for a COVID-19 test
A homogeneity hypothesis test formally tests if the populations have equal
Many statistical hypothesis tests and estimators of effect size assume that the variances of the populations are equal. This assumption allows the variances of each group to be pooled together to provide a better estimate of the population variance. A better estimate of the variance increases the statistical power of the test meaning you can use a smaller sample size to detect the same difference, or detect smaller differences and make sharper inferences with the same sample size.
When the test p-value is small, you can reject the null hypothesis and conclude that the populations differ in variance.