Bayesian statistics is a statistical approach that uses Bayes' theorem to update the probability of a hypothesis as more evidence or information becomes available. It combines prior knowledge, represented as a prior probability, with new data to produce a revised probability, known as the posterior probability. This method allows for a more flexible interpretation of uncertainty and can be applied in various fields, including medicine, finance, and machine learning. In Bayesian statistics, the process of updating beliefs is iterative. As new data is collected, the posterior probability can become the prior for future analyses. This continuous updating makes Bayesian methods particularly useful for decision-making in uncertain environments, where information evolves over time.