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Explore data distributions as a fundamental prerequisite for statistical testing in this 43-minute Python tutorial. Learn to distinguish between normal and non-normal distributions using visual assessment methods, and understand key statistical measures including skewness and kurtosis. Master normality testing techniques through the Shapiro-Wilk, Anderson-Darling, and D'Agostino-Pearson tests, and discover when and how to apply data transformations to prepare your datasets for analysis. Follow along with practical Python implementations using the UCI Wine Quality Dataset to gain hands-on experience with real-world statistical data analysis concepts essential for hypothesis testing.
