DataCamp Flash Sale:
50% Off - Build Data and AI Skills!
Grab it
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
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.
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.
