Frequentist methods are a statistical approach that interprets probability as the long-run frequency of events occurring in repeated experiments. This perspective focuses on the idea that probabilities can be estimated by observing how often an event happens over many trials. For example, if you flip a coin many times, the frequentist approach would estimate the probability of landing heads based on the proportion of heads observed. In frequentist statistics, parameters are considered fixed but unknown values, and inference is made through techniques like hypothesis testing and confidence intervals. Unlike Bayesian methods, which incorporate prior beliefs, frequentist methods rely solely on the data from the current experiment. This approach is widely used in fields such as economics, medicine, and social sciences.