Frequentist estimation is a statistical approach that focuses on the long-run frequency of events. It relies on the idea that probabilities are determined by the proportion of times an event occurs in repeated trials. For example, if you flip a coin many times, the frequentist approach would estimate the probability of getting heads based on the ratio of heads to total flips. In frequentist estimation, parameters are estimated using sample data, and confidence intervals are often constructed to indicate the reliability of these estimates. Unlike Bayesian methods, frequentist techniques do not incorporate prior beliefs or information, relying solely on the data at hand to make inferences about the population.