The more people you have at your company running good experiments, the better. However, not all of us have a data scientist that can look over out shoulders when running tests to mitigate errors. So it can be daunting for someone new to testing. I wanted to create a guide to help democratize A/B testing and to educate ambitious & aspiring experimenters, and I wanted it to serve two purposes: -A quick reference checklist that anyone, new or experienced, can use to reduce errors in experiments -A central and comprehensive guide to educate people in experimentation topics. Hope you enjoy! It's a bit cumbersome at 6k words, but I made it a sort of "choose your own adventure" based on your level and what topics you care about.
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