A/B Testing Fundamentals

Summary

Randomly splitting users into a control and variant group to measure a change's real causal impact — the gold standard for validating that a feature actually improved the metric you care about.

• 1Design an A/B test for a real feature idea, including control and variant.
• 2Learn the difference between correlation and causation, and why A/B tests solve for the latter.
• 3Understand common pitfalls: novelty effects, seasonality, and running tests too briefly.