Understanding A/B Testing: Principles, Design, and Decision Making at Netflix

This article is the second in a series about how Netflix uses A/B testing to make decisions and continuously innovate its product.

A/B testing is a simple controlled experiment where a hypothesis is tested by randomly assigning users to a control group (A) with the existing experience and a treatment group (B) with a new variation, such as an inverted box art on the TV UI.

Random sampling (simple random sampling) and random assignment ensure that the two groups are balanced on all dimensions that could affect the outcome, allowing any observed difference to be attributed to the change.

Metrics are chosen to reflect member engagement, such as click‑through rate, viewing time, or overall satisfaction, and secondary metrics are monitored to detect unintended effects.

The article emphasizes that A/B tests enable causal inference, helping product teams confidently roll out changes that improve long‑term member value while using guard‑rail metrics to prevent negative impacts.

It also discusses how ideas are turned into testable hypotheses, the importance of keeping all other factors constant, and how results guide decisions on whether to adopt, revert, or further investigate a new product experience.