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/

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(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