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 hypothesis test formally tests if the proportions in two or more populations are equal.
When one variable is an explanatory variable (X, fixed) and the other a response variable (Y, random), the hypothesis of interest is whether the populations have the same or different proportions in each category.
You can formulate the hypotheses in terms of the parameter of interest: odds ratio = 1, the ratio of proportions = 1, or the difference of proportions = 0 depending on the desired effect size estimate.
When the test p-value is small, you can reject the null hypothesis and conclude that the populations differ in the proportions in at least one category.
Tests for contingency tables larger than 2 x 2 are omnibus tests and do not tell you which groups differ from each other or in which categories. You should use the mosaic plot to examine the association, or partition the contingency table into a series of 2 x 2 sub-tables and test each table.