In this chapter, we talked about what A/B tests are and what are the challenges surrounding them. We went into some examples of how you actually measure the effects of variance using the t-statistic and p-value metrics, and we got into coding and measuring t-tests using Python. We then went on to discuss the short-term nature of an A/B test and its limitations, such as novelty effects or seasonal effects.
That also wraps up our time in this book. Congratulations for making it this far, that's a serious achievement and you should be proud of yourself. We've covered a lot of material here and I hope that you at least understand the concepts and have a little bit of hands-on experience with most of the techniques that are used in data science today. It's a very broad field, so we've touched on a little bit of everything there. So, you know, congratulations...
