Frequentist Statistics is a branch of statistics that interprets probability as the long-run frequency of events occurring in repeated experiments. It focuses on the idea that the true value of a parameter can be estimated through sample data, relying on methods like hypothesis testing and confidence intervals to draw conclusions. In Frequentist approaches, parameters are considered fixed but unknown, and the data is random. This means that the results can vary with different samples. Unlike Bayesian Statistics, which incorporates prior beliefs, Frequentist Statistics strictly uses the data at hand to make inferences about the population.