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 population parameters are different from the hypothesized values. For a multinomial distribution, the parameters are the proportions of occurrence of each outcome.
The null hypothesis states that the proportions equal the hypothesized values, against the alternative hypothesis that at least one of the proportions is not equal to its hypothesized value. When the test p-value is small, you can reject the null hypothesis and conclude that at least one proportion is not equal to its hypothesized value.
The test is an omnibus test and does not tell you which proportions differ from the hypothesized values.