Summary

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 again.

If you want to further your career in this field, what I'd really encourage you to do is talk to your boss. If you work at...