This module gives you a first look at the world of statistical modeling. It begins with a high-level overview of different philosophies on the question of 'what is a statistical model' and introduces you to the core ideas of traditional statistical inference and reasoning. At the end of the module, you will have an introductory understanding of important terms such as 'sample-to-population' (STOP) principle, sampling variation, and measures of statistical uncertainty. You will also get your first look at the ever popular t-test.

This module takes you beyond t-test into linear regression. By the end of the module, you will understand how linear regression is a generalization of the t-test.

A key reason that linear regression is so powerful is that it allows to adjust for multiple predictors at the same time. In Module 3, you will learn how to fit regression models for multiple predictors. You will see how to interpret the resulting model and how to use it to answer different questions about your data.