Frequentist inference is a statistical approach that focuses on the frequency or proportion of data. It relies on the idea that probabilities are determined by the long-run behavior of random events. In this framework, parameters are considered fixed but unknown values, and the goal is to make inferences about these parameters based on observed data. In frequentist inference, hypothesis testing and confidence intervals are common methods used to draw conclusions. For example, a null hypothesis is tested against an alternative hypothesis to determine if there is enough evidence to reject the null. This approach does not incorporate prior beliefs or information, distinguishing it from Bayesian inference.