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
Continuity corrections such as Yates X2are no longer needed with modern computing power.
Continuity corrections have historically been used to make adjustments to the p-value when a continuous distribution approximates a discrete distribution. Yates correction for the Pearson chi-square (X2) test is probably the most well-known continuity correction. In some cases, the continuity correction may adjust the p-value too far, and the test then becomes overly conservative.
Modern computing power makes such corrections unnecessary, as exact tests that use the discrete distributions are available for moderate and in many cases even large sample sizes. Hirji (Hirji 2005), states "An applied statistician today, in our view, may regard such corrections as interesting historical curiosities."
