Normality Tests
Normality tests are statistical procedures used to assess whether a dataset follows a normal (Gaussian) distribution, which is a fundamental assumption in many parametric statistical methods. They help determine if data can be reliably analyzed using techniques like t-tests, ANOVA, or linear regression that require normally distributed data. Common tests include the Shapiro-Wilk test, Kolmogorov-Smirnov test, and Anderson-Darling test, each with specific applications and sensitivity to sample size.
Developers should learn normality tests when working with data analysis, machine learning, or statistical modeling to validate assumptions before applying parametric methods, ensuring accurate results and avoiding model errors. They are crucial in fields like data science, A/B testing, and quality control, where decisions rely on statistical inference from data distributions. For example, in machine learning, checking normality can guide feature transformation or model selection to improve performance.