Your website demands constant decision-making—button colors, checkout flows, homepage designs, and countless other elements compete for your attention. You’ve likely learned that testing beats guesswork and random chance. What looks perfect in a design mockup can fail spectacularly with real customers.
While A/B testing and multivariate testing help you compare different versions of your site, A/A testing serves a different purpose. It measures the natural variability in your data when nothing changes. Without this baseline, you might mistake normal fluctuations for real results and conclude your design or copy tweak worked when it didn’t. A/A testing also confirms your testing platform works correctly.
Here’s more on what A/A testing is, why it matters for marketing optimization, and how to implement it.
What is an A/A test?
An A/A test is like an A/B test, but both versions of the asset are exactly the same. You split your traffic into two groups and show each group an identical webpage, email, or app screen. Since the content is the same, the goal isn’t to find a better version; it’s to confirm there’s no significant difference in performance between the identical samples.
Even with identical pages, one group might convert at 3.2% and the other at 3.4%, simply due to random variation—different people visiting at different times. This gives you a baseline for natural fluctuations. So if a future test shows a 5% lift from a new checkout button, you’ll know it’s likely a real improvement. But a 0.2% increase from changing a button color? That’s probably just noise.
What is an A/B test?
An A/B test—or split test—is the standard method for comparing two different versions, like two different homepage designs or newsletter subject lines. You show each version (A and B) to separate groups of visitors to identify the version that yields higher conversions or click-through rates. Whichever version wins gets rolled out to everyone. Unlike an A/A test, which uses identical versions, an A/B test tries a change and measures its impact.
You run the A/A test first and the A/B test second, both using the same testing software. Think of the A/A test as a calibration step. It shows how much your metrics naturally fluctuate when nothing changes. This way, when you run A/B tests, you can tell whether the result reflects a real improvement or just random variation.
Why run an A/A test?
- Establish baseline conversion rates
- Understand normal statistical variation
- Discover biases
- Test A/B testing software
An A/A test helps prevent false wins, like claiming your new homepage headline boosted conversions by 0.7% when that’s the kind of fluctuation you’d see even with no change. It also helps you catch potential problems with your testing platform. Here are the main uses:
Establish baseline conversion rates
You should know your site’s baseline conversion rates under normal conditions. An A/A test helps you determine these benchmarks in a controlled way. For example, you might run an A/A test on your current checkout page to measure the typical conversion rate from cart to purchase, without introducing any changes. Suppose in an A/A test, the control version yields 303 conversions out of 10,000 visitors (a 3.03% conversion rate) and the identical variant yields 307 conversions out of 10,000 (3.07%).
These two results are practically the same, as expected. You could conclude that your baseline conversion rate is around 3%. So if a future A/B test shows an increase from 3.03% to 3.07%, you’ll recognize it as likely just normal variation, not a meaningful lift.
Understand normal statistical variation
By running an A/A test, you can see how much outcomes can bounce around by chance. No matter how consistent your site is, there is always some random variation between groups of visitors. A/A testing quantifies this natural variability, so you know what “noise” looks like for your brand. In the example above, seeing a 3.03% versus 3.07% conversion is a tiny difference that shows no statistical significance.
Maybe one version happened to get more late-night shoppers or a few high-value customers—coincidences that can cause small bumps. This is normal. Knowing your site’s typical range of variation helps you interpret A/B test results and avoid mistaking random noise for real improvement.
Discover biases
A/A tests can surface significant bias or flaws in your experiment setup or how your audience is being split. A common issue is a sample ratio mismatch—when traffic isn’t splitting evenly or correctly between variants. For example, if your A/B testing tool is supposed to send 50% of shoppers to each page but an A/A test shows one “A” page getting 60% of the traffic, something is off.
This uneven split distorts results, introducing bias. Running an A/A test first reveals the tool wasn’t truly randomizing visitors—maybe due to a buggy integration or conflicting site scripts—helping you work out the bugs before you launch your actual A/B experiment.
Another type of bias an A/A test can uncover relates to audience segmentation. You might accidentally set up your test in a way that one variant is seen disproportionately by a certain segment—say, more new customers or more mobile users, while the other variant sees more returning customers or desktop users.
In an A/A test, both variants are the same and should perform the same across segments. If you spot a significant difference, it could mean your test targeting or randomization is inadvertently skewed.
Test A/B testing software
When you implement a new testing tool, running an A/A test confirms everything is correctly configured and set up. For example, an A/A test can verify your A/B tool really splits users 50/50 and that it’s recording conversions accurately. If it shows a big difference between two identical experiences, you know there’s a problem to resolve before running real experiments.
Consider this scenario: You’re about to A/B test a new navigation menu layout on your site. Before testing the new dropdown menu versus the old version, you decide to run an A/A test using the current menu in both versions.
If your testing software is working correctly, both groups of visitors should experience the dropdown menu similarly and have nearly identical click-through and conversion rates. But if menu “A” in the test shows a much higher add-to-cart rate than menu “A (duplicate)” for no reason, it might reveal a tracking bug. By surfacing these issues in an A/A test, you can fix them before running the real A/B test on the new versus old menu.
How to run an A/A test
Running an A/A test on your ecommerce site follows the same basic steps as an A/B test. The key difference is that you don’t introduce any new variation. Here’s how:
1. Generate identical pages
Decide what page or feature you want to test (like your homepage, checkout page, or an email campaign), and generate two identical variations of it.
Set up a new test in your A/B testing tool—like Shoplift or Optimizely—but rather than creating different variations, make both versions identical to your current page. Make sure there are no differences between the versions. Everything from the design and copy should be the same.
If you’re A/A testing an email, use the exact same subject line, pre-header text, and content for both email sends. The goal of this testing methodology is to eliminate any intentional differences so that the two experiences are indistinguishable to users.
2. Send to test group
Run the experiment by splitting site traffic between these two identical versions. Configure your A/B testing platform to divide your visitors 50/50 (or evenly among variants) and send each group to one of the versions.
For an A/A test, since both versions are duplicates, every user is essentially seeing the original experience—just through two different buckets for tracking purposes. The split needs to be random and span your typical audience. Include all the user segments you normally would (unless you specifically want to test a segment) so that the A/A test represents your overall site traffic.
Don’t rush. Let the A/A test run long enough to gather sufficient data. You need a minimum sample size to see meaningful variations. Treat it like a real A/B test, running it across days or weeks to reach the usual sample size you’d require for significance.
3. Assess your A/A results
Once your A/A test has run its course, dig into the data with a web analytics tool. Ideally, you’ll find that the two groups’ metrics are essentially the same or so close that no statistically significant difference can be declared—as expected because there was no real difference between them. Review the key performance indicators you tracked: conversion, click-through, and revenue per visitor.
You might see 2.5% versus 2.55% conversion, or an order value of $50.10 versus $49.80. These small differences are normal—and a good sign. It means your testing setup is working as it should. You’ve established a baseline conversion rate and confirmed that the testing tool isn’t introducing unexpected bias or errors.
Ideally, A/A test results show no clear winner, so a statistically significant difference means something outside of user preference caused it. When this happens, pause to investigate. Causes might include uneven traffic splitting, tracking glitches, or filtering problems.
Address the issue and rerun the A/A test until both variations perform equally before proceeding to A/B testing.
A/A test FAQ
What is an A/A test?
An A/A test is an experiment that splits your user base (or web traffic) between two identical versions (A versus A) of a page or feature. It’s used to verify that your testing setup is working correctly and assess baseline performance, rather than to find a better variant or a statistically significant result.
How do you perform an A/A test?
To perform an A/A test, set up a regular A/B test but use the same content for both the control and the variant. Then, run it like a normal test: Split your traffic evenly, run it long enough to gather sufficient data, and then confirm that the results for both groups are about the same. Any big difference means something’s wrong in the setup.
What’s an A/B test?
An A/B test is a way of comparing two different versions of something (like a landing page, ad, or email) to see which one gets better results. In an A/B test, version A is usually the original (control) and version B is a modified version. By measuring user response (like clicks or conversions), you can understand which version is more effective and use that winner moving forward.
