A/B Testing in R

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.
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4 Hours16 Videos60 Exercises8,606 Learners
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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. 1

    Chapter 1: Mini case study in A/B Testing

    Short case study on building and analyzing an A/B experiment.
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  2. 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.
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  3. 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.
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  4. 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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David CamposChester IsmayShon Inouye
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DataCamp offers interactive R, Python, Spreadsheets, SQL and shell courses. All on topics in data science, statistics, and machine learning. Learn from a team of expert teachers in the comfort of your browser with video lessons and fun coding challenges and projects.
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