Why 80% of A/B tests fail, how to 10X your experimentation velocity - Kristi Angel - The Data Scientist Show #088

Most experimentations fail, Kristi Angel shares her expertise on scaling experimentation and avoiding common A/B testing pitfalls. Learn five things that can help boost test velocity, designing impactful experiments, and leveraging knowledge repos. (Chapters below) Kristi Angel’s LinkedIn: ⁠https://www.linkedin.com/in/kristiangel/ Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career. Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠ Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠ (00:00:00) Intro (00:01:26) Why do most experimentations fail? (00:07:05) Mistakes in choosing metrics (00:10:05) Is revenue a good metric? (00:13:18) Split metrics in three ways (00:15:10) Daliana's story with too many category breakdowns (00:16:59) What makes the best data science team? (00:19:24) Data scientist work in silo vs in a data science team (00:21:15) Building a knowledge center (00:23:40) Example of knowledge center; nuance of experimentations (00:26:09) How many metrics and variants? (00:30:56) How to reduce noise - CUPED (00:33:01) Future of A/B testing (00:38:33) Q&A: Low statistical power

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A deep dive into data scientists' day-to-day work, tools and models they use, how they tackle problems, and their career journeys. This podcast helps you grow a successful career in data science. Listening to an episode is like having lunch with an experienced mentor. Guests are data science practitioners from various industries, AI researchers, economists, and CTOs of AI companies. Host: Daliana Liu, an ex-Amazon senior data scientist with 180k followers on Linkedin. Join 20k subscribers at www.dalianaliu.com to learn more about data science, career, and this show. Twitter @DalianaLiu.