Business Analytics iCademy: A/B Testing and ANOVA in R

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. Learning Outcomes - 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