A/B Testing and ANOVA in R
Estimated Time Commitment: 4 Hours
We will explore the marketing world through the process of design of experiments, A/B testing, data analysis with ANOVA, and hypothesis testing.
A well-designed experiment helps rule out competing explanations for observed differences between treatment groups, and leads to confidence in the ability to make inferences from the sample to the population. Thus, we will first introduce you to design of experiments. This idea is helpful for understanding A/B testing.
A/B testing is marketing parlance for test and control groups. We will introduce you to this idea, and how to use t-tests to evaluate if sample differences are statistically significant. We will also introduce you to Analysis of Variance (ANOVA), which is used to determine significant differences between two or more categorical groups. We will review various aspects of ANOVA, including the underlying assumptions, different types of ANOVA, and how the results can lead to inferences about the population.
Upon successful completion, you will be able to:
Identify ways to plan an experiment so that differences between treatments can be quantified
Develop an understanding of A/B testing and the approach to follow for carrying out an A/B test
Carry out t-tests and ANOVA tests for evaluating differences between experimental groups
Interpret test results for better managerial and managerial decision-making
Interview with Monica Panagos - A/B Testing and ANOVA in Practice
Introduction to the Skill
Glossary
Experiment Design - Key Concepts
Experiment Design - Controlling for Experimental Errors
Knowledge Check 1
A/B Testing Introduction
A/B Testing – Types of Tests
A/B Testing – R Example
ANOVA Introduction
Knowledge Check 2
One-Way ANOVA - Insect Spray Example
One-Way ANOVA - Insect Spray Example in R
Two-Way ANOVA - Tooth Growth Example
Knowledge Check 3
Instructions
Exercise Files
Debriefing
Conclusion
Final Quiz
Survey Instructions
Feedback Survey
Survey Verification
Next Steps