Parametric Statistics
Parametric statistics is a branch of statistical inference that relies on assumptions about the underlying distribution of data, typically assuming it follows a known probability distribution (e.g., normal distribution). It involves using parameters like mean and variance to make inferences, such as hypothesis testing and confidence intervals, and is widely applied in fields like psychology, medicine, and social sciences. This approach contrasts with non-parametric methods, which do not assume a specific distribution.
Developers should learn parametric statistics when working on data analysis, machine learning, or A/B testing projects that involve normally distributed data or require precise parameter estimation, such as in clinical trials or quality control. It is essential for tasks like t-tests, ANOVA, and regression analysis, where assumptions about data distribution are valid and lead to more powerful and efficient statistical tests compared to non-parametric alternatives.