Bayesian Statistics is a framework for statistical analysis that incorporates prior knowledge or beliefs into the process of inference. It uses Bayes' theorem to update the probability of a hypothesis as more evidence or information becomes available. This approach allows statisticians to make more informed decisions based on both existing data and prior insights. In contrast to traditional frequentist statistics, which relies solely on the data at hand, Bayesian methods provide a more flexible and intuitive way to model uncertainty. By treating parameters as random variables, Bayesian Statistics enables the incorporation of subjective beliefs and the quantification of uncertainty in a coherent manner.