1. Wrap-up: A/B testing in python
Congratulations! You've reached the end of the course, but certainly not the end of an exciting AB testing and learning journey. We've covered a lot, so let's summarize what we've learned.
2. A/B testing summary
In chapter 1, you learned the fundamental steps of AB testing and where it can and can't be used. You also learned how to define and estimate several categories of metrics, along with functions such as dot sample, dot corr, and plotting functions like seaborn's pairplot and heatmap.
In chapter 2, we focused on the experiment design process. You learned how to formulate strong hypotheses, understand concepts like error rates, power, and effect size, followed by the critical components of a power analysis for sample size calculations, as well as multiple comparisons corrections.
In chapter 3 you learned about common data cleaning techniques and exploratory analyses, sanity checks for internal and external validation, a framework for analyzing difference in proportions, and functions for running z-tests and estimating confidence intervals.
Finally, chapter 4 presented you with a framework for analyzing differences in means, and another for leveraging non-parametric tests when several assumptions aren't met. You also learned how to apply the Delta method for analyzing ratio metrics, and closed out with best practices and a list of more advanced topics to continue on your growth trajectory.
3. Congratulations!
Thank you for dedicating time to make it this far in the world of A/B testing, and I wish you the very best on this exciting learning and growth journey.