Frequentist Statistics
Frequentist statistics is a branch of statistical inference that interprets probability as the long-run frequency of events in repeated experiments. It focuses on using sample data to make inferences about population parameters through methods like hypothesis testing, confidence intervals, and p-values, without incorporating prior beliefs or subjective probabilities.
Developers should learn frequentist statistics when working on data-driven applications, A/B testing, or machine learning models that require rigorous validation, as it provides objective, repeatable methods for decision-making. It is essential in fields like software analytics, quality assurance, and scientific computing where empirical evidence from data is prioritized over subjective assumptions.