Posterior probability is a concept from Bayesian statistics that refers to the probability of a certain event or hypothesis being true after considering new evidence. It combines prior knowledge, represented by the prior probability, with the likelihood of observing the new evidence. This helps us update our beliefs based on the information we gather. For example, if a doctor has a prior probability of a patient having a certain disease, they can use test results to calculate the posterior probability. This updated probability gives a clearer picture of the patient's condition, allowing for better decision-making in their treatment.