How long should an A/B test run?

Until it reaches the required sample size for statistical significance — typically 2-4 weeks for most web applications. Never stop a test early because one variant "looks" better. Use a sample size calculator before starting.

What is a multivariate test?

A multivariate test changes multiple elements simultaneously (e.g., headline AND button color) and measures all combinations. It requires much larger sample sizes but reveals interaction effects between changes.

A/B Testing Explained

Controlled experiments that let data, not opinions, drive product decisions.

A/B Testing

A/B testing is an experimentation method where two or more variants of a feature, page, or flow are shown to different user groups simultaneously, and statistical analysis determines which variant performs better against a predefined metric.

Explanation

Instead of debating whether a blue or green button will drive more sign-ups, A/B testing lets data decide. Users are randomly assigned to groups: Group A sees the current version (control), Group B sees the variation (treatment). Both groups use the product naturally, and the system tracks a key metric — conversion rate, time on page, revenue per user, or any measurable outcome. Statistical significance is critical. Running an A/B test for too short a period or with too few users can produce misleading results. Before starting, calculate the required sample size based on: the baseline conversion rate, the minimum detectable effect (how small a change matters to you), and the desired statistical power (typically 80%) and significance level (typically 95%). Most tests need thousands of observations per variant to be reliable. A/B testing goes beyond button colors. Teams test pricing pages, onboarding flows, email subject lines, search algorithms, recommendation engines, and feature designs. Multivariate testing tests multiple changes simultaneously. The key pitfall is testing too many things at once (interaction effects make results uninterpretable) or peeking at results early and stopping the test prematurely (inflates false positive rates).

Bookuvai Implementation

Bookuvai integrates A/B testing infrastructure into projects that need data-driven optimization. We use feature flags as the targeting mechanism, with server-side assignment ensuring consistent user experiences. Analytics events track variant assignment and outcome metrics. Our AI PM helps define hypothesis, success metrics, and sample size requirements before each test to ensure statistically valid results.

Key Facts

Requires statistical significance — typically 95% confidence and 80% power
Sample size calculation should happen before the test starts, not after
Peeking at results and stopping early inflates false positive rates

Related Terms

Frequently Asked Questions

How long should an A/B test run?: Until it reaches the required sample size for statistical significance — typically 2-4 weeks for most web applications. Never stop a test early because one variant "looks" better. Use a sample size calculator before starting.
What is a multivariate test?: A multivariate test changes multiple elements simultaneously (e.g., headline AND button color) and measures all combinations. It requires much larger sample sizes but reveals interaction effects between changes.