# A/B Testing in R

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

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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.

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### Chapter 1: Mini case study in A/B Testing

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

Introduction50 xpGoals of A/B testing50 xpPreliminary data exploration100 xpBaseline conversion rates50 xpCurrent conversion rate day of week100 xpCurrent conversion rate week100 xpPlotting conversion rate seasonality100 xpExperimental design, power analysis50 xpRandomized vs. sequential50 xpSSizeLogisticBin() documentation50 xpPower analysis August100 xpPower analysis August 5 percentage point increase100 xp - 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.

Analyzing results50 xpPlotting results100 xpglm() documentation50 xpPractice with glm()100 xpDesigning follow-up experiments50 xpFollow-up experiment 1 design50 xpFollow-up experiment 1 power analysis100 xpFollow-up experiment 1 analysis100 xpPre-follow-up experiment assumptions50 xpPlot 8 months data100 xpPlot styling 1100 xpPlot styling 2100 xpFollow-up experiment assumptions50 xpConversion rate between years100 xpRe-run power analysis for follow-up100 xpRe-run glm() for follow-up100 xp - 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.

A/B testing research questions50 xpArticle click frequency monthly100 xp'Like' click frequency plot100 xp'Like' / 'Share' click frequency plot100 xpAssumptions and types of A/B testing50 xpBetween vs. within50 xpPlotting A/A data100 xpAnalyzing A/A data100 xpConfounding variables50 xpExamples of confounding variables50 xpConfounding variable example analysis100 xpConfounding variable example plotting100 xpSide effects50 xpConfounding variable vs. side effect50 xpSide effect load time plot100 xpSide effects experiment plot100 xp - 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.

Power analyses50 xpLogistic regression power analysis100 xppwr.t.test() documentation50 xpT-test power analysis100 xpStatistical tests50 xpLogistic regression100 xpT-test100 xpStopping rules and sequential analysis50 xpWhat is a sequential analysis?50 xpSequential analysis three looks100 xpSequential analysis sample sizes100 xpMultivariate testing50 xpPlotting time homepage in multivariate experiment100 xpPlotting 'like' clicks in multivariate experiment100 xpMultivariate design statistical test100 xpA/B Testing Recap50 xp

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