Why product teams use controlled A/B experiments to make launch decisions

What do product teams gain from running A/B tests? Explain the main benefits. ```hint What else changed? Picture the week before and the week after a launch, and list everything besides your feature that could have moved the numbers. Then ask what splitting users at random does to that comparison. ``` ### Clarifying Questions - Should I stick to the benefits, or also say when an A/B test is the wrong tool, so the case is balanced? - Should I ground the answer in a particular kind of product, such as a B2B customer-messaging tool with relatively few customer accounts, where sample size limits what a test can detect, or a high-traffic consumer app? - Does A/B testing here include multivariate tests and long-running holdout groups, or only a two-variant split? ### What a Strong Answer Covers - Why random assignment lets a team attribute a change in a metric to the product change, in contrast with before-and-after analysis - Measuring the size of an effect and its uncertainty, including effects smaller than normal metric noise - Limiting risk by exposing a fraction of users and watching guardrail metrics before a full rollout - How experiments change decision-making: settling debates with evidence and making bolder ideas cheaper to try - Honest limits: sample size, interference between users, short test windows and changes that cannot be tested ### Follow-up Questions - A test shows a statistically significant 0.3% lift in your primary metric and a small drop in a guardrail metric. Do you ship? - Your product has a few thousand business customers rather than millions of consumers. How does that change the way you run experiments? - When would you ship a change without testing it?