Overview

Introduction

The central limit theorem states that the averages of samples drawn from any distribution tend towards a normal distribution.

This concept surfaces in many settings and you likely have some intuition for understanding this topic already! In science class, for instance, we often perform multiple trials of an experiment because reporting the average result over multiple trials is less error-prone than reporting the result of one trial.

This module will introduce the formal statistics behind this concept and will show how this concept most frequently shows up in data science in the form of confidence intervals. The first section will cover discrete and continuous probability distributions. The second section will show examples of the central limit theorem in action. The third and final section will show how the central limit theorem allows us to construct confidence intervals.