Using real-world examples from a wide range of domains including law, medicine, and football, you’ll learn how data scientists make conclusions about unknowns based on the data available. Often, the data we have are not complete, yet we’d still like to draw inferences about the world and quantify the uncertainty in our conclusions. This is called statistical inference. In this course, you will learn the framework for statistical inference and apply them to real-world data sets. Notably, you will learn how to conduct hypothesis testing—comparing theoretical predictions to actual data, and choosing whether to accept those predictions. You will utilize the power of computation to conduct simulations by which you can evaluate theories or hypotheses about how the world works. This course will teach you the power of statistical inference: given a random sample, how do we predict some quantity that we cannot observe directly? You will also learn how to by quantifying the uncertainty in the conclusions you draw from hypothesis testing. This helps assess whether patterns that appear to be present in the data actually represent a true relationship in the world, or whether they might merely reflect random fluctuations due to chance. Throughout this course, we will go over multiple methods for estimation and hypothesis testing, based on simulations and the bootstrap method. Finally, you will learn about randomized controlled experiments and how to draw conclusions about causality. The course emphasizes the conceptual basis of inference, the logic of the decision-making process, and the sound interpretation of results.