Data Science Techniques: How extreme is your data point?

In this article, I will discuss Outliers and Model Selection. When I was an undergraduate student of Science at the University of Waterloo, my lab professor always said to keep all data, even if it is an outlier. This is because we want to keep the authenticity of the data and to be able to make scientific discoveries. Many discoveries have been found on accidents, so let's explore whether you should delete that data point because you drop your hamburger on your experiment or not. Running regression is one thing, but choosing the suitable model and the correct data is another.