Statistical Tests in Machine Learning

When it comes to statistics in machine learning, a common approach to accept or reject a null hypothesis is to check for the p-values and give a result without really having an idea of what goes on in the background. Without getting into any kind of fancy jargons or mathematical technicalities, this article attempts to sum up the intuition behind statistics using some real life examples especially for people from a non-statistics background. Why do we need hypothesis testing? But what if suddenly, Dunkin' happens to shut down because Krispe Kreme claims the weight of their donuts is less than what Dunkin' claims. How do we choose sides?