# AA Tests >[!Abstract] Why you care? >Running A/A tests is a critical part of establishing trust in an experimentation platform. The idea is so useful because the tests faily many times in practice, which leads to re-evaluating assumptions and identifying bugs. The idea of an A/A test is simple: Split the users into two groups as in a regular A/B test but make B identical to A (hence the name A/A test). If the system is operating correctly, then in repeated trials about 5% of the time a given metric should be statistically significant with p-value less than 0.05. When conducting t-tests to compute p-values, the distribution of p-values from repeated trials should be close to a uniform distribution. This is related to [[AB Testing]] **Source:** Trustworthy Online Controlled Experiments