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-Squa...Read more.
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The techniques and tools covered in A/B Testing in R are most similar to the requirements found in Data Scientist data science job advertisements.
