4 Common A/B Testing Mistakes (And How to Fix Them)

A/B testing is a powerful technique for optimizing your website and marketing campaigns. By comparing two versions of a page or element, you can determine which one performs better based on real user behavior.

However, A/B testing is not as simple as it sounds. There are many pitfalls that can lead to false conclusions and wasted effort. In fact, a study by Convert found that only about 1 in 7 A/B tests produces a significant result.

In this post, we‘ll examine four of the most common A/B testing mistakes and show you how to avoid them. By the end, you‘ll be equipped to run more rigorous, reliable tests that drive meaningful improvements for your business.

Mistake #1: Ending Tests Too Early

One of the most prevalent A/B testing errors is stopping a test as soon as one variation pulls ahead. You see a 20% lift after a couple days and get excited to implement the winner.

However, calling a test too early is dangerous because it increases the risk of false positives. The smaller your sample size, the more likely that the results are due to random chance rather than a true difference between variations.

Here‘s an example to illustrate:

Suppose you‘re testing two versions of a landing page:

  • Page A (control): 10% conversion rate
  • Page B (variation): 15% conversion rate

After 100 visitors, Page B appears to be the winner. But is this difference statistically significant? Let‘s do the math:

  • Page A: 10 conversions / 100 visitors = 10% conversion rate
  • Page B: 15 conversions / 100 visitors = 15% conversion rate

A common significance threshold is 95%, which means there‘s only a 5% chance that the observed difference is due to randomness.

Using a sample size calculator, we find that we would need at least 430 visitors per variation (860 total) to detect a 5% difference with 95% significance.

So in this case, 100 visitors is nowhere near enough to conclude that Page B is better. The early lead could easily be a fluke.

The Fix: Use Statistical Significance

To avoid premature conclusions, you need to use statistical significance as your standard for ending A/B tests. This means waiting until you have a large enough sample size to be confident that the results are real and reliable.

Here‘s how to do it:

  1. Before starting a test, use a sample size calculator to determine the minimum number of visitors or conversions needed per variation. Aim for at least 95% significance.

  2. Keep the test running until each variation reaches the target sample size. Don‘t peek at the results or get tempted to stop early.

  3. Once you reach the sample size, check the significance level (p-value) reported by your testing tool. If it‘s under 0.05 (95% significance), you can conclude there‘s a real difference.

  4. Optional: To be extra rigorous, consider additional checks like setting a minimum detectable effect, correcting for multiple comparisons, or using Bayesian inference.

Most A/B testing tools have built-in significance calculators, but they may use different models and assumptions. For important tests, it‘s worth doing the math yourself or consulting a statistician to be sure.

Mistake #2: Testing Too Many Variables

Another frequent mistake is trying to test too many changes at once by pitting drastically different designs against each other, like this:

Image of two very different website designs

On the surface, this seems efficient. Why test one thing at a time when you can test ten and get answers faster?

The problem is that the more variables you change, the harder it is to isolate their individual impact. If the variation performs better, you won‘t know whether it was due to the headline, image, button color, layout, or a combination.

You might conclude that the winning variation is better as a whole, but you‘ll have a hard time extracting specific, applicable insights. You‘ll also have no way to know if you could get even better results by mixing and matching elements from each variation.

The Fix: Limit Variables Per Test

As a best practice, you should test only 1-3 closely related variables at a time, especially for high-stakes pages. The goal is to minimize confounding factors so you can make clear, confident conclusions about what works and what doesn‘t.

For example, let‘s say you want to test your call-to-action (CTA) button. Here are some good ways to limit variables:

  • Test 2-3 variations of the button copy while keeping everything else the same
  • Test 2-3 variations of the button color while keeping the copy constant
  • Test 2-3 variations of the button size or placement on the page

By isolating each element, you can learn exactly which version of each performs best. You can then combine the winners into a best-of-breed variation to maximize impact.

This doesn‘t mean you can never test multiple variables, just that you need to be strategic about it. Some situations where it may make sense to test more variables:

  • Lower-traffic pages where reaching significance would take too long
  • Radical redesigns where the goal is to compare overall approaches
  • Personalization where different versions are targeted to different segments
  • Complex applications where user flows span multiple pages

Even in these cases, though, try to limit the differences between variations as much as possible. The more controlled your test, the more reliable your insights will be.

Mistake #3: Not Segmenting Visitors

Another common oversight is failing to segment visitors when running A/B tests. Many marketers simply lump everyone together and show the same variations to all traffic.

The problem is that different types of visitors may respond very differently to your tests. For example, mobile users might prefer a different experience than desktop users. New visitors might behave differently than returning ones.

If you blend all these groups together, you risk muddying the results and missing key opportunities. A variation that works well for one segment might tank for another, leading to a neutral overall result.

Imagine you‘re testing two versions of a product page:

  • Control: Long-form sales copy with detailed feature explanations
  • Variation: Short, benefit-focused copy with social proof and urgency

When you analyze the results, you see no significant difference in conversion rates. But when you segment by traffic source, a different picture emerges:

Segment Control CVR Variation CVR Lift
Organic 2.5% 5% +100%
Paid Search 3% 2% -33%
Referral 1% 1.5% +50%
Email 10% 5% -50%
Overall 4% 4% 0%

In this example, the variation actually worked great for organic traffic (+100% lift) and pretty well for referral traffic (+50% lift). But it bombed for paid search (-33% lift) and email (-50% lift), cancelling out the gains.

If we hadn‘t segmented, we would have concluded that the variation had no impact and missed a big win for organic traffic. We also would have overlooked a serious problem with paid search and email that needs further investigation.

The Fix: Segment by Key Dimensions

The solution is simple: use your testing tool‘s targeting options to create meaningful visitor segments and run separate tests for each one. This allows you to tailor your experience to different groups and uncover valuable insights that would be lost in aggregate data.

Some common dimensions to consider segmenting by:

  • Traffic source: Direct, organic search, paid search, social, email, referral
  • Device: Desktop, mobile, tablet
  • New vs. returning: First-time visitors, repeat visitors, customers
  • Geography: Country, region, metro area
  • Persona: Job role, industry, company size

Start with high-level segments and drill down into more granular ones over time. Be careful not to slice too thinly or you‘ll struggle to reach significance. A good rule of thumb is to aim for segments that make up at least 10-20% of your total traffic.

In addition to running separate tests for each segment, you should also analyze the results of all tests by segment. This can help you identify patterns and trends across different audience groups.

For example, you might find that mobile visitors consistently prefer shorter copy and more visual content, while desktop visitors engage more with longer formats. You can use these insights to inform your overall content strategy and personalization efforts.

Mistake #4: Not Having a Clear Hypothesis

Perhaps the most fundamental A/B testing mistake is not having a clear, specific hypothesis for each test. Many teams treat testing as a random guessing game, throwing spaghetti at the wall to see what sticks.

Without a hypothesis, you have no way to prioritize test ideas or interpret the results in a meaningful way. You might stumble on some winners by chance, but you‘ll have no clue why they won or how to replicate the success.

A good hypothesis statement has three parts:

  1. The change you‘re making (independent variable)
  2. The effect you expect to see (dependent variable)
  3. Your rationale for why this change will produce this effect

Here‘s an example:

If we shorten the checkout form from 8 fields to 4, then the conversion rate will increase by 10%, because reducing friction will make visitors more likely to complete their purchase.

See how this provides a clear, testable prediction grounded in a plausible causal mechanism? You can easily design a test around this hypothesis and measure the results.

Compare that to a vague notion like "Let‘s test a shorter form and see what happens." Without a concrete expectation, it‘s hard to tell if the test succeeded, failed, or produced a meaningful learning.

The Fix: Follow a Hypothesis-Driven Process

To avoid aimless testing, you need to follow a structured process anchored in explicit hypotheses. Here‘s a simple four-step framework you can use:

  1. Gather ideas: Collect A/B test ideas from analytics, user feedback, heuristic analysis, and competitive research. Focus on areas with high potential impact.

  2. Form hypotheses: For each idea, articulate a specific, measurable hypothesis using the format described above. Be clear about the variable you‘re changing, the effect you expect, and your rationale.

  3. Prioritize tests: Score each hypothesis on two criteria: 1) Potential impact if true, and 2) Ease of implementation. Prioritize tests that are high-impact and low-effort first. Consider dependencies between tests.

  4. Analyze results: After running a test, revisit your hypothesis and compare it to the actual results. Did the test confirm or disprove your prediction? What did you learn? Document your findings and share them with your team.

By forcing you to make educated guesses, this process ensures that every test is grounded in strategic intent. It keeps you focused on the most impactful areas while providing a feedback loop to refine your intuitions over time.

Of course, not every hypothesis will be validated. In fact, most A/B tests fail to produce a significant improvement. But that‘s okay – a failed test is still a valuable learning opportunity. By understanding where your assumptions were wrong, you can form better hypotheses next time.

The key is to treat A/B testing as an iterative, ongoing process of discovery rather than a one-off tactic. With each test, you‘re not just fishing for a win, but building a deeper understanding of your audience and what makes them tick.


A/B testing is a powerful tool for data-driven optimization, but it‘s not foolproof. By falling prey to these four common mistakes, you can easily waste time and money on unproductive tests and false conclusions:

  1. Ending tests too early before reaching significance
  2. Testing too many variables at once instead of isolating changes
  3. Not segmenting visitors to uncover key differences
  4. Not having a clear hypothesis to guide each test

The good news is that these mistakes are easily preventable by following rigorous statistical practices, limiting test complexity, leveraging visitor segments, and adhering to a hypothesis-driven process.

By avoiding these pitfalls and continuously refining your approach, you can unlock the full potential of A/B testing to drive meaningful, sustainable growth for your business. So what are you waiting for? Go forth and test with confidence!