Statistical Inference is going to be a course that covers many of the foundational ideas that are involved with extracting generalizable information from data. And so, the course will cover things like basic probability, likelihoods common distributions, confidence intervals, hypothesis tests, bootstrapping and power. These are the sort of fundamental ideas that you often hear coming up when people are reporting data analysis. So, for example, we'll talk about sort of the way that you model mathematically coin flips or proportions. We'll also talk a little bit about how you model more continuous distributions, things like, with the normal distribution which you've probably heard about. As a way to measure sort of the variability about a large number of different things including IQ and height and things like that. And then we'll talk about things like bootstrapping, where you actually use the data itself to sort of create measures of variability that you can use to sort of decide how generalizable are the answers that you get from performing any particular analysis.