In the field of machine learning, testing assumptions is a important step in the model development process. It helps ensure that the underlying assumptions of the chosen algorithm are valid and that the model's predictions are reliable. In this tutorial, we discuss two major algorithms commonly used for testing assumptions in machine learning: the Shapiro-Wilk