Posterior probability is the probability of a certain event occurring after taking into account new evidence or information. It is a key concept in Bayesian statistics, where it updates our beliefs based on prior knowledge and observed data. The posterior probability is calculated using Bayes' theorem, which combines the prior probability and the likelihood of the new evidence. In practical terms, if you want to determine the likelihood of a disease given a positive test result, the posterior probability helps you understand how the test result influences your belief about having the disease. This approach allows for more informed decision-making in various fields, including medicine and machine learning.