Interactive Course

A/B Testing in R

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

  • 4 hours
  • 16 Videos
  • 60 Exercises
  • 2,919 Participants
  • 4,700 XP

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Course Description

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.

  1. 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.

  2. 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.

  1. 1

    Chapter 1: Mini case study in A/B Testing

    Free

    Short case study on building and analyzing an A/B experiment.

  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.

  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.

  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.

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Page Piccinini
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.

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