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
Asymptotic p-values are useful for large sample sizes when the calculation of an exact p-value is too computer-intensive.
Many test statistics follow a discrete probability distribution. However, hand calculation of the true probability distributions of many test statistics is too tedious except for small samples. It can be computationally difficult and time intensive even for a powerful computer. Instead, many statistical tests use an approximation to the true distribution. Approximations assume the sample size is large enough so that the test statistic converges to an appropriate limiting normal or
A p-value that is calculated using an approximation to the true distribution is called an asymptotic p-value. A p-value calculated using the true distribution is called an exact p-value. For large sample sizes, the exact and asymptotic p-values are very similar. For small sample sizes or sparse data, the exact and asymptotic p-values can be quite different and can lead to different conclusions about the hypothesis of interest.
When using the true distribution, due to the discreteness of the distribution, the p-value and confidence intervals are conservative. A conservative interval guarantees that the actual coverage level is at least as large as the nominal confidence level, though it can be much larger. For a hypothesis test, it guarantees protection from type I error at the nominal significance level. Some statisticians prefer asymptotic p-values and intervals, as they tend to achieve an average coverage
closer to the nominal level. The problem is the actual coverage of a specific test or interval may be less than the nominal level.
Although a statistical test might commonly use an approximation, it does not mean it cannot be calculated using the true probability distribution. For example, an asymptotic p-value for the Pearson X2 test uses the chi-squared approximation, but the test could also compute an exact p-value using the true probability distribution. Similarly, although the Fisher test is often called the Fisher exact test because it computes an exact p-value using the hypergeometric probability
distribution, the test could also compute an asymptotic p-value. Likewise, the Wilcoxon-Mann-Whitney test often computes an exact p-value for small sample sizes and reverts to an asymptotic p-value for large sample sizes.