13 Common AB Testing Mistakes (& How to Avoid Them)

Ready to put your pre-testing planning to good use? Now it’s time to navigate the A/B testing process itself. 

Let’s explore some common mistakes to avoid at this stage so you can get the most out of your testing journey.

6. Testing the wrong page

Picking the wrong page for your test is a recipe for wasted resources and inconclusive data.

Choosing pages with low traffic or minimal impact on the conversion funnel can result in negligible changes. This makes it difficult to discern meaningful insights from the test results.

The fix: Focus on pages critical to your conversion funnel and with high-impact potential. Analyze A/B test results and behavior, and prioritize tests that address key conversion points.

7. Testing too many hypotheses at once

It’s tempting to address multiple questions or test several elements in one A/B test. This can lead to analysis paralysis because too many variables and versions muddle the results. 

You can’t isolate the impact of a single change when you’re testing multiple elements at once.

The fix: Maintain clarity and simplicity. Focus on one key metric and a single hypothesis per test for fair comparison. This ensures clear results and easier interpretation.

8. Having too many test variants

Don’t confuse multivariate testing with A/B testing. Multivariate testing optimizes multiple versions simultaneously, while A/B testing focuses on one variable. 

Including too many variations in a single A/B test makes it difficult to pinpoint which change is causing a specific effect.

The fix: Stick to a small number of variations (typically two to four). This balances thorough exploration with efficient testing and quicker results.

9. Running multiple tests on the same page

Imagine you’re testing a new button on your website. If you also change the layout of the page in the middle of the test, it becomes difficult to know if the new button or the new layout is causing the results you see. 

This can throw off your entire test and make the data useless.  

The fix: Ensure data purity by focusing on one test at a time. This allows you to clearly isolate the impact of each change.

10. Ending your test before reaching statistical significance

Not enough testing (stopping the test too early) or even changing parameters in the middle of a test can be tricky.

In those cases, the differences you see between your variants might just be down to random luck, not because of the changes you made.

We call this a “false positive.” By testing for a longer period with more visitors, you get a clearer picture (reach statistical significance) and can be more confident the changes you see are real.

The fix: Don’t cut your test short! Run tests for a sufficient period (at least a week) to gather enough data and reach statistical significance. Limited data leads to unreliable results.

11. Not running tests constantly

People’s habits and what they like change all the time—that’s customer behavior for you!

By regularly testing different parts of your website (like buttons on your landing page or the checkout page for mobile users), you can find ways to make it more enjoyable and easier to use for visitors, tapping into those behavior changes. 

This can lead to higher conversion rates, and that’s your goal, right? 

The fix: Embrace a culture of continuous testing. Regular A/B testing keeps your website optimized and your conversions climbing!

If you lack inspiration, you can find some fresh A/B testing ideas here to keep your website optimization journey moving forward.