Beyond the A/B Test: Experiment Design for Your Toughest Questions

Most experimentation teams have ideas they’ve deemed “untestable”, usually because they don’t fit the default experiment design of an A/B test. We’re here to challenge that. 

Pritul Patel, Senior Customer Data Scientist at Statsig, will show you how to make faster, safer decisions using different experiment patterns. We’ll cover where randomization simplifies messy assumptions, where you’ll need alternate designs, and more.

Bring an idea or question your team didn’t end up testing, and you’ll leave knowing the best experiment design to answer it.

Who this is for:

• Data scientists and analysts responsible for picking the right methodology

• Leaders setting experimentation standards

What you’ll learn:

• How to match your causal method to the problem when standard A/B tests break down.

• What to consider past the immediate conversion metric.

• A working overview of alternate experiment designs and when to use each one.