Understanding A/B testing in UX research

There are endless ways to design digital product interfaces— you can use different colors, typography properties, layouts, and text. We should choose the most user-friendly, efficient, and high-quality design variant that also supports business goals. If you end up with the two best design versions for a specific part of the digital product interface during your UX process, how would you fairly decide which one to use?

A/B testing, the most popular quantitative UX decision-making technique, helps you to seamlessly get the userbase’s help to find the most suitable design version if you struggle choosing which design variant is better.

Let’s understand what’s A/B testing, when you should (or shouldn’t) do it, and how to conduct one for your design scenarios properly, avoiding common A/B testing mistakes.

Editor’s note: This post was updated in May 2026 by LogRocket author Shalitha Suranga to provide a more comprehensive guide to A/B testing in UX design, including when to use A/B tests, when to avoid them, common mistakes, UX experiment metrics, and how A/B testing compares with usability testing.

What is A/B testing?

In UI/UX design, A/B testing, also known as split testing, refers to a quantitative UX research method that compares two design versions based on real user behavior to identify the top-performing design version. It’s named with “A/B” since two versions, A and B, are evaluated:

• Version A: Control, the original design version that usually already exists
• Version B: Variant, the new design variant that competes with version A

A/B testing selects the winning version using metrics like click-through rate (CTR), conversion rate, bounce rate, and average session duration:

Benefits of A/B testing

A/B testing is a powerful, user-centric decision-making technique with proven benefits to incrementally extend existing user interfaces:

• Fair, effective, data-driven decision making: No need to spend time arguing which design is the best in internal design discussions — A/B testing uses real user behavioral data to decide which design variant is best
• Increased conversion rate: The design variant that the users love most always wins. So, when you are evaluating CTAs or similar elements, the winning design always gets the highest conversion rate compared to the other design versions
• Frictionless, fast user testing: A traditional usability test requires providing slow qualitative feedback, but A/B testing helps evaluate designs faster by seamlessly evaluating user behavior with various quantitative decision metrics
• Low-risk, low-cost evaluation: A/B testing can be magically automated to produce low-risk and low-cost evaluations — no need to worry about surprising negative user reactions or overrunning the design budget
• Better understanding of users: A/B testing is a simple quantitative decision-making technique, but the evaluation happens on a live product by targeting real users, so using proper metrics helps you extract important design details that you could use even later, e.g., what colors or labels perform best for the targeted user base

A/B testing is a low-risk, low-cost, fast, and effective decision-making method that uses real users’ behavioral data

A/B testing steps

A/B testing goes through five key phases: defining goals and metrics, defining a hypothesis, creating design variants, experimentation, and analysis:

Let’s understand what exactly happens inside each step:

Defining goals and metrics

You should know the goals of your A/B testing case before beginning it. Basically, answer the “Why should I do an A/B test?” question to get a clear understanding of goals. e.g., “I am doing an A/B test to understand which CTA label performs better.”

To form a strong hypothesis for your A/B test, you need to select a good metric that calculates the final scores for each variant. Metric changes can affect each other, so selecting several guardrail metrics along with the primary one helps you confidently decide which design variant is the best in all aspects.

Here are some common metrics used in most A/B tests:

• Conversion rate: Percentage of users who took the desired action, e.g., signing up, downloading a software product, etc.
• Click-through rate (CTR): Percentage of users who clicked on a specific element
• Task success rate (TSR): Percentage of users who completed a specific task
• Bounce rate: Percentage of users who left the product or specific UI segment without continuing interaction. High bounce rate is a sign of poor UX
• Session duration: How long a user spends with the product or a specific feature in a single user session; the length of a single user session
• Time on task: How long a user takes to complete a specific task. Shorter times usually show better UX properties and productivity

You aren’t limited to these metrics; you can use other metrics like scroll depth, retention rate, etc., or create your own metric based on your A/B testing goals

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Defining a hypothesis

A/B testing hypothesis refers to a concise assumption that states why changing a specific design element increases/decreases a specific metric. Like in other quantitative analysis scenarios, A/B testing uses an alternative hypothesis with a null hypothesis to statistically conclude whether version A or B is better.

A good A/B testing hypothesis should contain the following details:

• Changes for a design element: Changes you make to only one design element to produce version B, usually by changing label, color, border, typography property, imagery, layout, general behavior, etc. Choose only one design element; otherwise, you’ll have to perform multivariate testing instead of A/B testing
• Metric changes: Will the metric increase or decrease after changing the design element
• Rationale: Reason why you assume the metric changes after adjusting the particular design element

You can use the following generic template to define your alternate hypothesis:

If we [changes for a design element], then [metric changes], because [rationale]

Here are some examples:

• If we use the primary CTA label as “Download for <OS name>”, the conversion rate will increase because the action feels instant and personalized
• If we use an infinite loading for the music list, the session duration will increase, since it’s more seamless than the existing pagination

Create a solid hypothesis using the above format and carefully analyze your existing design and new idea.

Creating the new variant (or variants)

A/B testing is usually done for the original, existing design against a new proposed design variant, so you usually have to newly design only one variant at this stage. If you do A/B testing for two new ideas, you should create two design variants.

Create a new design variant based on your hypothesis and prepare it for A/B testing experimentation, which is handled by developers. This isn’t a special design — use Figma or your team’s preferred design tool to create the high-fidelity prototype for the particulate UI region.

Experimentation

At this stage, you are collaborating with developers to run the A/B testing. You should send the following things in an A/B testing experimentation plan to developers to help them conduct an accurate A/B test:

• Hypothesis to understand the task context
• High-fidelity designs for variant B (both A and B if you are testing two new ideas)
• Experimentation duration, e.g., 2 weeks
• Traffic distribution rules, e.g., targeting only mobile devices, a specific OS, new users, etc.
• All metrics you are interested in, including a primary metric, secondary metrics, and guardrail metrics

Once A/B testing has been started, 50% of users will receive version A, and the other 50% will get version B through A/B testing tools (e.g., Optimizely) or other feature deployment strategies (via load balancers, feature flags, etc.) that the development team uses.

At the end of the experimentation, the development team will provide values for all requested metrics.

Analysis and implementation

Evaluate your hypothesis based on A/B testing results — it can be accepted or rejected. Even though you can simply identify which design variant is better just by comparing the values of the metric, you should write a statistical analysis by stating statistical confidence, especially because you conduct traditional, statistical, quantitative research under UI/UX A/B testing.

When you write the A/B testing conclusion, you should mention whether your alternative hypothesis is accepted (null hypothesis is rejected) or rejected (null hypothesis is accepted) using a statistical result interpretation method like p-value or Bayesian method.

If the hypothesis is accepted, version B is better than the original version A, and you can continue the version B deployment process, collaborating with developers. Otherwise, you can keep the original version A and document the summary for future A/B testing scenarios and general design considerations.

A/B testing example

Let’s simulate an A/B test for a sample scenario by following the above steps to understand the process further.

Assume that you need to do an A/B test to prove that changing the primary CTA label to “Try it for free” from “Get started” will improve the conversion rate.

Step 1: Defining goals and metrics

The goal is to prove that changing the primary CTA label to “Try it for free” will increase conversion rate. The primary metric is conversion rate. Guardrail metrics can be ones that validate revenue impact, like revenue per visitor (RPV), freemium conversion rate, etc.

Step 2: Defining the hypothesis

• Alternate hypothesis: If we use the primary CTA label as “Try it for free”, the conversion rate will increase because the new label implies that the user can get started with the product without adding payment card details
• Null hypothesis: Changing the primary CTA label has no impact on conversion rate

Step 3: Creating the new variant (or variants)

Creating a new landing page design variant by changing the CTA label

Step 4: Experimentation

Running the test for 2 weeks for new users

Step 5: Analysis and implementation

Assume that this sample A/B test produced the following result table:

Metric	Version A	Version B
No. of visitors	10000	10000
Conversions	500	600
Conversion rate	5%	6%

According to the result, version B performed better than version A, increasing the conversion rate by 20%. Let’s write a statistical conclusion for these results by using the popular p-value comparison method.

Use an A/B testing tool or an A/B testing p-value calculator to calculate the p-value. I used AB Testguide online calculator:

The calculated p-value: 0.0019. The calculated p-value is less than the common significance threshold, 0.05, so the 20% conversion rate increment of version B is statistically significant and isn’t likely due to chance.

Hypothesis accepted, using “Try it for free” as the primary CTA label increases the conversion rate by 20%. So, version B can be deployed.

When should designers use A/B tests?

Designers can effectively improve UX by using A/B testing for the following cases:

• Expects a boolean decision: A/B testing outputs the acceptance or rejection of a hypothesis, so it will be the optimal method if you expect a yes/no answer for a design decision at the end, e.g., the new background imagery improves (or doesn’t) user engagement
• Quantitative scoring is possible: If quantitative metrics can be used for data collection, A/B testing becomes the most practical UX evaluation method
• High traffic UI regions: When the sample size increases, the final result becomes more statistically significant, so A/B testing delivers more confident summaries for high-traffic products
• A clear hypothesis can be formed: If the design evaluation can form a clear, testable hypothesis, the math and final reasoning in A/B testing will be more accurate
• Avoiding internal design debates: People think differently, so while one person argues that version B is better, another will still support version A. A/B testing is a fair, user-centric way to decide a winning version

When should designers avoid A/B tests?

Conducting A/B tests for the following cases won’t deliver statistically confident and overall effective results:

• Expects qualitative feedback: A/B tests output boolean statements, so they won’t work in scenarios where you expect descriptive output, e.g., evaluating the pros and cons of a new feature
• Low traffic UI regions: Statistical significance decreases with the sample size decrement, so we can’t form a design decision confidently using A/B tests on low traffic elements
• Large UI/UX improvements: A/B testing results can’t effectively describe user behavior in detail for complex UI/UX improvements, since it focuses on selecting one from two versions of a single design element. However, you can do an A/B test for a large UI/UX change if you already did a usability test for the whole change, e.g., evaluating the new checkout flow of a hotel booking app
• Early-stage designs: A/B testing is done on the real user base, so testing incomplete features can negatively affect the overall usability and product reputation
• Straightforward design decisions: You shouldn’t overuse A/B testing; if version B can be selected based on general UI/UX design principles, you should use version B after internal agreements, e.g., using a more accessible CTA button variant if the current one’s color blends with the hero section background

A/B testing vs. Usability testing

A/B and usability testing can both be used to evaluate designs; however, they are two different testing strategies that produce two different result types:

Comparison factor	A/B testing	Usability testing
Description	A quantitative UX research method that helps designers decide the best version by evaluating two design versions on the live product	A qualitative UX research method that helps designers get insights about design ideas or improvements from a small number of participants
Research method type	Quantitative	Qualitative
Goal	Deciding which design version is better	Identifying user pain points
When it’s done	After shipping the product	In any stage of the product design process
Type of participants	Actual users	Actual or potential users and other product stakeholders
Number of participants	Many, usually thousands	Less, usually 5-10
Experiment environment	Live product	Prototype, staging environment, or a special session of the live product
Results	A boolean result, yes/no, which describes whether or not the proposed design variant is better than the original version	Insights about pain points and efficiency that help improve usability factors

A/B testing identifies whether or not a new design element variant is better than the existing one using numbers (quantitative data), and usability testing produces insights about user behavior within a digital product.

Common A/B testing mistakes

Even if you initiate an A/B test for the right scenario, it can still produce incorrect results if you don’t carefully avoid the following common A/B testing mistakes:

• Running the test during special periods: Traffic during holidays, seasonal periods, and other special periods can generate results that don’t properly represent normal user behavior
• Stopping the test early: Early test stops can decrease expected traffic, reduce the statistical significance of results, and result in a false positive interpretation
• Overlapping A/B tests: If you run more than one A/B test at the same time and they overlap, you won’t get accurate results for any A/B test since one design element variant can affect another, and vice versa
• Incorrect experiment configuration: Poor experiment design, misinterpretation of the A/B test experimentation plan, or developers’ mistakes can make the real A/B test so different than the planned one
• Evaluating only one metric: Not selecting guardrail metrics along with the primary metric can lead to wrong decision-making that negatively affects overall product usability

FAQs

Here are some common questions that you may ask yourself when you are getting started with the UI/UX A/B testing process:

Can I test more than two design versions with A/B testing?

Yes, you can test any number of design variants, but it’s called A/B/n testing — not the standard A/B testing

How big should an A/B testing sample size be?

Large enough to get a statistically significant result. You don’t need to worry about this if your product receives a very large amount of traffic; otherwise, verify the sample size requirement using an A/B testing sample size calculator like Optimizely’s

How long should you run the experiment?

Until you reach the sample size requirement or statistical significance. In a product that receives good incoming traffic, running A/B tests for at least 1–2 weeks is recommended

Will version B design variant affect the real usability score and conversion rates?

Yes, but a good version B can drastically reduce negative effects

Parallel vs. Sequential: Which traffic distribution is better?

Running a single A/B test in parallel by splitting the traffic is better since sequential traffic can lead to time-based bias

Should we always use the fixed 50%/50% traffic splitting logic?

No, you can use dynamic A/B testing traffic allocation to increase traffic for the winning version to optimize the traffic. It’s called a multi-armed bandit test

Conclusion

In this article, we discussed how to get started with A/B testing for evaluating two design versions during the product development process. A/B testing is a widely used design evaluation technique that tech giants use to improve usability and product revenue. A/B testing is a simple technique that any digital product can benefit from, but it should be initiated properly with an efficient, automated flow to effectively select the most suitable design variant while maintaining seamless user involvement.

If you are going to run your first A/B test on your digital product, build an efficient, automated A/B testing workflow by collaborating with developers first, using a dedicated A/B testing tool like Optimizely, VWO, etc., or building an organization-specific flow using developers’ deployment tools like Kubernetes. Then, identify whether an A/B test is really needed for your decision-making scenario, start with a clear goal, define a strong hypothesis, run the experiment properly, and conclude with proper statistical analysis.