Course A/B Testing in R

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.


This online course about A/B Testing in R covers a key part of what a future data analyst would require.

In this course, you will learn the foundations of A/B testing, including hypothesis testing, experimental design, and confounding variables. You will also be exposed to a couple more advanced topics, sequential analysis and multivariate testing. The first dataset will be a generated example of a cat adoption website. You will investigate if changing the homepage image affects conversion rates (the percentage of people who click a specific button). For the remainder of the course you will use another generated dataset of a hypothetical data visualization website.

Enroll now in this A/B Testing in R course, and don’t miss the opportunity of learning with the best, as Page Piccinini is. With 60 enriching exercises, 16 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.

Chapter 1: Chapter 1: Mini case study in A/B Testing
Short case study on building and analyzing an A/B experiment.
Chapter 2: Chapter 2: Mini case study in A/B Testing Part 2
In this chapter we’ll continue with our case study, now moving to our statistical analysis. We’ll also discuss how to do follow-up experiment planning.
Chapter 3: Chapter 3: Experimental Design in A/B Testing
In this chapter we’ll dive deeper into the core concepts of A/B testing. This will include discussing A/B testing research questions, assumptions and types of A/B testing, as well as what confounding variables and side effects are.
Chapter 4: Chapter 4: Statistical Analyses in A/B Testing
In the final chapter we’ll go over more types of statistical tests and power analyses for different A/B testing designs. We’ll also introduce the concepts of stopping rules, sequential analysis, and multivariate analysis.
Chapter 5:
A/B Testing in R. Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.

Page Piccinini

Senior Data Scientist at Classy

I am a senior data scientist at Classy. I received my PhD from the Linguistics Department at the University of California, San Diego in 2016, and am interested in using new (and old) types of statistical models to explain and predict complex data sets, whether they be individually based (e.g. acoustic information) or population based (e.g. speakers of more than one language). My primary language is R, but American English is a close second.