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# 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.

4 Hours16 Videos54 Exercises

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## 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.

## 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.

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1. 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.

Play Chapter Now
A/B testing and design
50 xp
A/B subject design
50 xp
Format and histograms
100 xp
Considerations in A/B testing
50 xp
Type I error in A/B tests
50 xp
Sampling
100 xp
Family-wise error rate
100 xp
Power and sample size
50 xp
Power impacts
100 xp
Determine the ideal sample size
100 xp
Regression to the mean
100 xp
2. 2

### Comparing Groups

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

3. 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.

4. 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.

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Datasets

absentcovidwebdatagamedataSiteSales

Collaborators

Lauryn Burleigh

Cognitive 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.
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