In this section, we'll be discussing the central limit theorem, which is essential to our understanding of normal distribution. Normal distribution is an important formula for the study of even basic statistics in data science. Data science, at its heart, is mathematical. We're transitioning away from the technical aspects of Haskell and file formats. First let's look at the central limit theorem before we introduce normal distribution, and then we're going to be exploring the parameters of normal distribution. So, here is the definition of the central limit theorem as per Wikipedia:
The central limit theorem (CLT) states that, given certain conditions, the arithmetic mean of a sufficiently large number of iterates of independent random variables, each with a well-defined (finite) expected value and finite variance, will be approximately...
