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

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Running A/B tests can feel like scientific magic for optimizing your website. It’s a proven way to boost conversion rates and increase revenue.

But what if, despite your best intentions, your website split testing is unknowingly hurting your results? 

Here at OptiMonk, we’ve seen it happen countless times—and yes, we’ve even made a few AB testing mistakes ourselves!

Learning from these stumbles is crucial, which is why we’ve put together this guide to share the 13 most common A/B testing mistakes and show you exactly how to avoid them.

Let’s get down to it!

13 common A/b testing mistakes

Before testing

Before you hit the “test” button, take a step back and consider a few mistakes that could trip up your AB testing efforts during the planning stage. 

1. Not testing at all

The first (and maybe the biggest) mistake you can make is not running tests at all. 

Many businesses shy away from split testing due to perceived complexity, lack of technical expertise, or resource limitations. But this can be a major conversion killer.

So what’s wrong with not testing? You’re relying on assumptions, gut feelings, or outdated practices. This can lead to missed opportunities, ineffective strategies, and ultimately, lost revenue. 

Without A/B testing, it’s impossible to know what truly resonates with your audience, what drives engagement, and what causes friction in the customer journey.

The fix: Start small! Run simple tests to showcase the potential return on investment (ROI) from making decisions based on data. A testing tool like OptiMonk’s Smart A/B Testing feature can even automate tests, minimizing resource drain.

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2. Missing a hypothesis

Ever run a new test without a clear goal? It’s like driving with no destination—you might get somewhere, but it’s unlikely to be where you want to go. 

This approach often leads to inconclusive split testing results.

A hypothesis is essentially an educated guess that predicts the outcome of a test based on existing data or insights. It provides a framework for what you’re testing and why, ensuring that your efforts are aligned with your business objectives. 

Without it, you’re left with random experimentation, which can produce confusing, contradictory, or meaningless results.

The fix: Always begin with a specific, measurable hypothesis that aligns with your business objectives. This ensures your test has a clear purpose and a metric to measure positive results.

3. Performing tests with low traffic

Imagine conducting a survey with only 10 participants. Would you trust the results? 

Similarly, a small sample size from low-traffic pages can lead to inconclusive A/B tests and unreliable data.

When you perform tests with low traffic, you don’t gather enough test data to reach statistical significance. This means the results are not reliable indicators of future performance. 

Low-traffic tests can lead to false positives or false negatives. Both outcomes waste resources and can be misleading.

The fix: Prioritize high-traffic landing pages for initial tests to achieve statistical significance. Alternatively, consider extending the test duration to gather enough data for statistically significant results.

4. Not considering mobile users

With the rise of mobile browsing, neglecting mobile users in your A/B tests is a fatal flaw. Mobile users have different needs and user behavior compared to desktop users.

According to recent studies, mobile devices generate over half of all website traffic globally. 

Mobile users interact with websites differently due to screen size, touch navigation, and varying loading speeds. If your A/B tests are only optimized for desktop users, you’re missing out on insights that could enhance the user experience for a substantial portion of your target audience.

The fix: Ensure your tests are optimized for mobile and reflect the usage patterns of your mobile audience.

5. Pushing something live before testing

Adding new elements to a landing page or changing your value proposition without thorough testing can be tempting, especially under pressure. 

However, launching untested changes can negatively impact the user experience and the customer journey, and you might miss crucial optimization opportunities.

Any alteration to your website can impact the customer journey. If changes are not tested, they might create unexpected roadblocks. This can reduce conversion rates and frustrate users.

The fix: Always validate your ideas through A/B testing before full-scale deployment. This ensures you’re making data-driven decisions that truly benefit your users and your bottom line.

During testing

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.

After testing

You’ve run your test, and now comes the exciting part: finding those valuable insights and putting them to good use!

But before you celebrate, let’s avoid these common post-testing mistakes to ensure you get the most out of your efforts.

12. Discarding your test results

Every A/B test is a learning opportunity, even if it doesn’t give you the results you hoped for.

While it might be tempting to ignore those results, you may have been overlooking something important. Each test period generates valuable insights and data that you can learn from, which can ultimately affect your business.  

The fix: By analyzing all your test results, including those that seem unsuccessful, you can figure out why your initial idea might not have worked. This can give you valuable insights to use in future tests and marketing campaigns. 

Learning from these “unsuccessful” tests helps you improve your approach and get better results in the long run.

13. Not iterating on the test

Each A/B testing effort, successful or not, is a stepping stone in the optimization journey. Don’t treat them as isolated events, especially when considering the potential impact of multiple variables. 

You don’t want to take your results, make a single change, and call it a day. Instead, treat it as a continuous process.

The fix: Continually refine your testing strategy based on your findings. Use learnings to iterate on your tests, enhance the user experience, and optimize your conversion rates. It’s all about continuous improvement!

3 bonus tips for running effective A/B tests

Now that you’ve seen all the common mistakes and learned how to fix them, we’re here to give you 3 bonus tips on improving your A/B testing efforts even further. 

1. Leverage analytics 

Use tools like Google Analytics to track key metrics (conversion rate, page load time, etc.) and gain deeper insights into user behavior.

2. Start small, scale up 

Begin with simple tests and gradually increase complexity as you gain experience. This helps you avoid A/B testing mistakes and build confidence.

3. Continuous improvement

Don’t stop after one test. Use your learnings to refine your marketing strategy and iterate on tests for ongoing optimization.

By avoiding these common A/B testing mistakes, you can ensure your tests deliver accurate results that fuel positive outcomes. Remember, A/B testing is a journey, not a destination.

Wrapping up

Congratulations—you’re now in the driver’s seat and ready to make the most out of your marketing campaigns. We’ve explored the A/B testing landscape, identified common roadblocks, and equipped you with the knowledge to navigate them. 

If you need a powerful ally, OptiMonk’s user-friendly A/B testing features can streamline the process, helping you gather insights, save time, and achieve the best results. 

Create your free account today and put your testing hat on!