p-values in Normality Tests using Python

This video explores p-values for Beta distributions that look Normal using a Python Script. Using Monte-Carlos trials, Beta distributions are tested for a range of sample sizes and dispersion (standard deviations) to find out what portion of 1,000 trials have p-values greater than 0.05 using the Shapiro-Wilks Normality test. A link to the Python Jupyter Notebook at my Github site is below: https://github.com/dmadvoc/PDFs/blob/main/p-value.ipynb Chapters 0:00 Introduction 2:48 Monte-Carlo of a Normal PDF by Sample Size with Normality Tests 4:35 Monte-Carlo of a Normal-like Beta Distribution by Sample Size with Normality Tests 5:55 Monte-Carlos of Beta Distributions at different dispersions with Normality Tests 9:41 Monte-Carlos of Beta Distributions at different dispersions with percent of p-values gt 0.05