# A/B Testing in R

Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.

Start Course for Free4 Hours16 Videos54 Exercises

## Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.## Loved by learners at thousands of companies

## Course Description

A/B testing is a common experimental design for human behavior research in industry and academia. A/B tests compare two variants to determine if the measurement shows different performance and if measurements vary in a meaningful way. By learning about A/B testing and presenting the results, you can make data-driven decisions and predictions.

In this course, you’ll learn what questions the A/B tests can address, the important considerations to be aware of in A/B tests, how to answer the questions at hand, and how to visualize the data. You’ll also learn how to determine the sample size needed in an experiment, conduct analyses appropriate for the data and hypothesis at hand, determine if the results can be regarded with confidence, and present the results to an audience regardless of statistical background.

This course covers parametric and non-parametric A/B tests, such as t-tests, Mann-Whitney U test, Chi-Square test of independence, Fisher’s exact test, and Pearson and Spearman correlations. Additionally, you’ll explore a power analysis for each test.

As you progress, you’ll also learn to run linear and logistic regressions to predict outcomes based on data and previous findings.

By the time you complete this course, you’ll have a thorough understanding of A/B tests, the analyses you can perform with them, and how to relay the results with data visualizations.

## Build an Understanding of A/B Design

In this course, you’ll learn what questions the A/B tests can address, the important considerations to be aware of in A/B tests, how to answer the questions at hand, and how to visualize the data. You’ll also learn how to determine the sample size needed in an experiment, conduct analyses appropriate for the data and hypothesis at hand, determine if the results can be regarded with confidence, and present the results to an audience regardless of statistical background.

## Learn How to Analyze A/B Test Data

This course covers parametric and non-parametric A/B tests, such as t-tests, Mann-Whitney U test, Chi-Square test of independence, Fisher’s exact test, and Pearson and Spearman correlations. Additionally, you’ll explore a power analysis for each test.

## Predict Outcomes Based on Data

As you progress, you’ll also learn to run linear and logistic regressions to predict outcomes based on data and previous findings.

## Present Results to Any Audience with Visualizations

By the time you complete this course, you’ll have a thorough understanding of A/B tests, the analyses you can perform with them, and how to relay the results with data visualizations.

- 1
### Introduction to A/B Tests

**Free**Gain an understanding of A/B tests and design. Learn about the aspects to be aware of to ensure appropriate handling of the data and analyses.

- 2
### Comparing Groups

Learn common analyses to compare A/B groups. Understand the appropriate approach to each test given their assumptions and limitations.

Independent-sample t-test50 xpIndependent t-test sample size100 xpIndependent t-test analysis100 xpMann-Whitney U test50 xpMann-Whitney U analysis100 xpMann-Whitney U power assessment100 xpt-test vs. Mann-Whitney U test100 xpChi-Square test of independence50 xpChi-Square test of independence sample size100 xpChi-Square test of independence analysis100 xpFisher's Exact test50 xpFisher's Exact test analysis100 xpChoosing tests100 xp - 3
### Associations of Variables

Learn to analyze the trend and relationship of variables in A/B groups. Understand how to assess and present the results to any audience.

Introduction to correlations50 xpCorrelation notes100 xpCorrelation coefficient100 xpPearson correlation50 xpPearson sample size100 xpAssessing Pearson assumptions100 xpPearson ignoring groups100 xpPearson within groups100 xpSpearman-rank correlation50 xpSpearman correlation analysis100 xpCorrelation options100 xpReporting A/B test results50 xpChoosing a data visualization50 xpPlots for analyses100 xpCreating bar plots for A/B design100 xpCorrelation and plotting100 xp - 4
### Regression and Prediction

Understand the basis of regression and regression lines. Learn to run regressions, predict data based on the regression model, and visually present the results.

Introduction to linear regression50 xpScatter and points100 xpRegression facts50 xpLinear regression50 xpLinear regression model100 xpLinear predictions100 xpMultiple linear regression100 xpLogistic regression50 xpLogistic regression model100 xpLogistic probabilities100 xpPlotting regression50 xpLogistic plot100 xpLinearity100 xpWrap-up50 xp

Lauryn Burleigh

See MoreCognitive Neuroscientist

Lauryn has PhD in Cognitive Neuroscience from LSU. They are interested in using analyses to make data driven decisions and making data and statistics accessible. Currently, they are applying for data science and user experience research positions.

## What do other learners have to say?

## Join over 13 million learners and start A/B Testing in R today!

## Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.