A/B testing is a widely used experimental design in both industry and academia for researching human behavior. It involves comparing two variants to determine if there are significant differences in performance and if the measurements vary in a meaningful way. By gaining knowledge about A/B testing and effectively presenting the results, individuals can make informed decisions and predictions based on data. This course aims to provide a comprehensive understanding of A/B design. Participants will learn about the specific questions that A/B tests can address, the key considerations to keep in mind during these tests, how to analyze the data to answer the research questions, and how to effectively visualize the results. Additionally, participants will gain insights into determining the appropriate sample size for an experiment, conducting analyses that are suitable for the data and hypothesis at hand, assessing the confidence level of the results, and presenting the findings to an audience regardless of their statistical background. The course covers various parametric and non-parametric A/B tests, including t-tests, Mann-Whitney U test, Chi-Square test of independence, Fisher's exact test, and Pearson and Spearman correlations. Participants will also explore power analysis for each test, enhancing their ability to predict outcomes based on data. Furthermore, participants will learn how to run linear and logistic regressions to make predictions based on data and previous research findings. An important aspect of this course is the emphasis on effectively presenting the results to any audience. Participants will gain the skills to create impactful data visualizations that effectively communicate the findings of A/B tests. By the end of the course, participants will have a comprehensive understanding of A/B tests, the various analyses that can be performed using them, and the ability to present the results using data visualizations.