A/B Test

Contributor

Shiva Manjunath,

What is an A/B Test?

An A/B test, also called a split test, is a method of comparing two or more versions of a digital experience to find out which one performs better. In a simple A/B test:

Version A is the control. It’s the current version with no changes.
Version B is the variant. It contains one change, like a new button color, headline, or layout.

Users are randomly split between versions. The goal is to see which version drives more conversions, clicks, engagement, or other key metrics.

A/B testing is used in websites, apps, marketing campaigns, and even backend systems like recommendation engines. It’s the foundation of data-driven decision-making in product, design, and growth teams.

How A/B Testing Works

Running a reliable A/B test involves a series of clear steps:

Develop a hypothesis. Define what you believe will happen if you change something.
Create your control and variant(s). Control = original version. Variant = new version with one major change.
Randomly assign users. Use persistent, randomized assignment to ensure your test groups are statistically similar.
Run the test. Show each group their respective version during the same time period.
Track metrics. Measure performance using relevant metrics like conversion rate, bounce rate, or revenue per user.
Analyze the results. Use statistical methods to determine if the observed differences are real or due to chance.
Roll out the winner. If a variant outperforms the control with statistical significance, promote it to production.
Repeat. Apply what you’ve learned and plan your next test.

Why A/B Testing Is Valuable

A/B testing is widely used because it:

Helps teams test ideas without guesswork
Reduces the risk of shipping harmful changes
Provides user insights you didn’t think to ask for
Helps catch backend or UX vulnerabilities
Gives hard data to support product and design decisions
Builds more inclusive and optimized experiences
Aligns with agile, continuous improvement workflows
Can lead to major gains in conversion, retention, and satisfaction
Encourages a culture of testing, learning, and innovation

A/B Test vs Other Experiment Types

Multivariate Testing (MVT): Tests many combinations of multiple elements at once.
A/A Testing: Compares two identical versions to validate setup and traffic bucketing.
Split URL Testing: Sends traffic to entirely different URLs instead of modifying one page.

A/B testing is the simplest and most trusted method, especially for single-variable comparisons.

A/B Testing Examples

A/B tests can be applied almost anywhere:

Changing a headline on a landing page
Swapping the color or text of a call-to-action button
Testing different layouts for product detail pages
Adding or removing testimonials for social proof
Comparing a video landing page vs. static image
Testing personalized vs. non-personalized homepage experiences

“Your “A” version is your control and has no changes. You test this against your “B” version, which has an element/page/component different from the control. You can run them side-by-side to see what impact the change has.

An A/B test can quantify the impact of the change as long as proper statistics are employed. If the correct principles are followed, confidence in decision-making is unparalleled, and A/B testing is seen as the gold standard among test methods. Some people may opt out of this ‘gold standard’ due to difficulty setting up an A/B test (it can be costly and/or time-consuming) or lack of knowledge to set up and analyze an A/B test.”

Shiva Manjunath, Host of From A to B Podcast

Best Practices for A/B Testing

To run a statistically valid and trustworthy A/B test:

Start with a simple, specific, falsifiable hypothesis
Keep your test and control stable throughout—no mid-test changes
Run tests long enough to reach statistical significance
Use guardrail metrics to catch unintended effects
QA your test before launch to prevent visual or functional bugs
Make sure you have a large enough sample size
Run A/A tests to validate your testing platform and methodology
Use both quantitative (e.g., conversion rates) and qualitative (e.g., user feedback) data
Be transparent—document your assumptions, hypotheses, and outcomes
Don’t interpret results too early—peeking increases false positives
If the test fails, learn from it—every test gives you insights