A/B Testing

Experimenting with variants:​ collect reliable information on real users’ behaviour with relatively little resources.

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Evaluative method
Suggested Time

One week per feature

Required Expertise

Statistical expertise


A launched product or a prototype


Developers, product manager, tester

Nice to have
These methods might be important for some innovations only.


A/B tests consist of a randomized experiment that usually involves two variants (A and B), although the concept can be also extended to multiple variants of the same variable. ​


It is a relatively quick real-time test for product ideas. Via A/B testing you can settle a design team conflict, take informed, user-focused decisions, get mostly quantitative data about your designs, and improve user experience quickly.​


You are in the middle of designing your product, and two rivaling ideas seem equally attractive – for example, whether to use text or an image to convey an information, or two different styles of text. You have done some prior research and observation, that led to a hypothesis about how to fix a problem or improve an aspect of your product that you would like to test. ​


You will need the tech team on board to publish two versions of your prototype and collect the necessary information on how your users reacted. You’ll also need someone with the statistical knowledge to analyze any quantitative results and understand what they mean. Qualitative A/B tests should be conducted by experienced UX researchers in order to gather unbiased information and insights.​

How much time

Depending on the question and A/B testing variant, the test might have to run for several weeks before you have sufficient data to analyze or it might be done in a few hours.​

Why it’s useful

Produces very straight forward results by comparing two alternatives​​
Democratizes design and distributes decision-making power to your users

Potential challenges

You, or your team need some statistical literacy to design and analyze quantitative tests​

Is this for you?

Get step-by-step guidance, expert tips and best practice examples for effective A/B testing.

It’s like testing crop varieties

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