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DataCamp

Introduction to A/B Testing in R

via DataCamp

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Overview

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

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.

Syllabus

Chapter 1: Mini case study in A/B Testing
-Short case study on building and analyzing an A/B experiment.

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: 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: 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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