Frequentist Estimation
Frequentist estimation is a statistical inference approach that interprets probability as the long-run frequency of events in repeated experiments. It focuses on estimating unknown parameters of a population using sample data, typically through methods like maximum likelihood estimation (MLE) or method of moments. This framework does not incorporate prior beliefs and relies solely on observed data to make inferences about parameters.
Developers should learn frequentist estimation when working on data-driven applications, A/B testing, or machine learning models that require statistical validation, such as confidence intervals or hypothesis testing. It is essential for tasks like estimating model parameters in linear regression, analyzing experimental results in software testing, or building predictive models where repeatability and data-centric inference are prioritized over prior knowledge.