Classical Statistics
Classical statistics is a branch of statistics based on frequentist inference, which interprets probability as the long-run frequency of events. It includes foundational methods like hypothesis testing, confidence intervals, and parametric models (e.g., t-tests, ANOVA, linear regression) that rely on assumptions about data distributions. This approach is widely used for making inferences from sample data to populations without incorporating prior beliefs.
Developers should learn classical statistics when working on data analysis, A/B testing, or machine learning projects that require rigorous hypothesis validation and uncertainty quantification. It is essential for tasks like analyzing experimental results, building predictive models with interpretable parameters, or ensuring statistical significance in business metrics, particularly in fields like finance, healthcare, or social sciences where frequentist methods are standard.