What is an A/B Test?

This post explains the fundamental concept of A/B testing, including random assignment, control vs. treatment groups, and the importance of comparing metrics. It highlights how A/B tests enable causal statements by holding all other factors constant, contrasting this with non-experimental rollout methods. The post also touches on the idea generation process for experiments and the need for careful metric selection, including both hypothesis-specific and general engagement metrics.