R Tutorial: A/B Testing in R

Want to learn more? Take the full course at https://learn.datacamp.com/courses/ab-testing-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi! My name is Page Piccinini. I'm a data scientist, and I'll be your instructor for this course on A/B Testing in R. A/B testing is a powerful way to try out a new design or program changes before making final decisions. In this course, we'll go over the fundamentals of A/B testing so you can get started on building and analyzing your own A/B experiments. Before getting into A/B testing, let's talk about what it is and why it's useful for you. A/B testing is a framework for you to test different ideas for how to improve upon an existing design, often a website. With A/B testing you're able to take a set of new ideas, test them with a new experiment, statistically analyze the results to confidently say which idea is better, update your website or app to use the winning idea, and then continue the cycle over again. What's key to remember is A/B testing is not something you do just once. You want to be constantly updating your website or app to maximize things like conversion rates or usage time. With A/B testing you will always be making minor updates to push those numbers up. While A/B testing is often discussed in the context of websites and tech, really it can be used in any context where you have a question you want to test and then make updates accordingly. A/B testing is just an experimental design. You could A/B test two different fertilizer types in your garden, or secretly test two coffee brands at work to see which people like more. The world is your A/B testing playground! In Chapter 3 I'll go over some more example uses of A/B testing. Now, let's walk through a simplified set of steps with a hypothetical experiment. In future chapters, we'll see how A/B testing can be more complicated than our hypothetical example here. We'll be covering A/B tes