Most students do not find it hard to understand descriptive statistics but struggle with probability and hypothesis testing. The aim of this course is to help students to gain a deeper understanding of these tougher topics. Since the course assumes a basic understanding of descriptive statistics it does not cover these topics but starts with a section on probability. It then proceeds with discrete and continuous distributions, including the binomial, the Poisson and the normal distributions. When students have developed a proper understanding of probability and distributions, specifically the normal distribution, they are well prepared to understand sampling distributions and the central limit theorem which plays essential roles in estimation and hypothesis testing. When students have mastered the first part of the course, they are well prepared to understand estimation and hypothesis testing which form the last part of the course. The section on estimation covers point estimates and confidence intervals. The hypothesis testing section covers both tests for one population parameter and for two population parameters. It also explains important concepts like type I and type II errors, as well as the p-value of a test.

Throughout the course the focus is on understanding. It makes use of many simulations and visual illustrations to effectively communicate the course content. Some of the examples that are covered in the lectures are also demonstrated in Excel.