A-B Testing in R Data Camp

Overview

Context

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 A-B Testing in R course offered by Data Camp, 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.

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.

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.