Frequentist Inference
Frequentist inference is a statistical 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 hypothesis testing and confidence intervals. This framework assumes parameters are fixed but unknown, and inferences are based solely on observed data without incorporating prior beliefs.
Developers should learn frequentist inference when building data-driven applications, conducting A/B testing, or performing statistical analysis in fields like machine learning, data science, and experimental research. It is essential for tasks such as validating model performance, determining statistical significance in experiments, and making data-informed decisions in software development, as it provides objective, repeatable methods for inference without subjective prior assumptions.