Statistical Analysis in Python - 361 - Understanding Data Distributions (Statistical Analysis in Python: Tutorial 3)

Statistical Analysis in Python: Tutorial 3 – Understanding Data Distributions In this third video of the Statistical Analysis in Python series, we take a deep dive into understanding data distributions, an essential step before applying any statistical test. Topics covered: Normal vs. non-normal distributions Visual methods for assessing normality Skewness and kurtosis Normality tests: Shapiro-Wilk, Anderson-Darling, D’Agostino-Pearson When and how to apply data transformations We use the UCI Wine Quality Dataset to demonstrate these concepts with real-world Python code. Ideal for anyone working with statistical data analysis or preparing for hypothesis testing. Code: https://github.com/bnsreenu/python_for_microscopists/blob/master/361_Understanding_Data_Distributions.ipynb