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9 Common A/B Testing Mistakes to Avoid

A/B testing mistakes

A/B testing is one of the most effective ways to move business metrics in a positive direction and increase the inward flow of revenue. However, as stated above, A/B testing demands planning, patience, and precision. Making silly errors can cost your business time and money, which you can’t afford. To prevent that from happening, we have compiled a list of common A/B testing mistakes that people often make right below.

9 A/B testing mistakes to avoid

1. Not planning your optimization Roadmap

1.1. Invalid hypothesis

In A/B testing, a hypothesis is formulated before conducting a test. All the next steps depend on it: what should be changed, why should it be changed, what the expected outcome is, and so on.

If you start with the wrong hypothesis, the probability of the test succeeding will go down.

1.2. Taking others’ word for it

Imagine someone else changed their sign-up flow and saw a 30% uplift in conversions. But it is their test result, based on their traffic, their hypothesis, and their goals.

Here’s why you should not implement someone else’s test results as is onto your website: no two websites are the same – what worked for them might not work for you. Their traffic will be different; their target audience might be different; their optimization method may have been different than yours, and so on.

2. Testing too many elements together

Running multiple tests at the same time is one of the many A/B testing mistakes that industry experts have cautioned against. Testing too many elements of a website together makes it difficult to pinpoint which element influenced the test’s success or failure the most.

The more the elements tested, the more needs to be the traffic on that page to justify statistically significant testing. Thus, prioritization of tests is indispensable for successful A/B testing.

3. Ignoring statistical significance

If gut feelings or personal opinions find a way into hypothesis formulation or while you are setting the A/B test goals, it is most likely to fail. Irrespective of everything, whether the test succeeds or fails, you must let it run through its entire course so that it reaches its statistical significance.

For a reason, that test results, no matter good or bad, will give you valuable insights and help you plan your upcoming test in a better manner.

You can get more information about the different types of errors while dealing with the maths of A/B testing.

4. Using unbalanced traffic

Businesses often end up testing unbalanced traffic. A/B testing should be done with the appropriate traffic to get significant results. Using lower or higher traffic than required for testing increases the chances of your campaign failing or generating inconclusive results.

5. Testing for incorrect duration

Based on your traffic and goals, run A/B tests for a certain length of time to achieve statistical significance. Running a test for too long or too short a period can result in the test failing or producing insignificant results.

Because one version of your website appears to be winning within the first few days of starting the test does not mean that you should call it off before time and declare a winner.

Letting a campaign run for too long is also a common blunder that businesses commit. The duration for which you need to run your test depends on various factors like existing traffic, existing conversion rate, expected improvement, etc.

6. Failing to follow an iterative process

A/B testing is an iterative process, with each test building upon the results of the previous tests. Businesses give up on A/B testing after their first test fails. But to improve the chances of your next test succeeding, you should draw insights from your last tests while planning and deploying your next test. This increases the probability of your test succeeding with statistically significant results.

Additionally, do not stop testing after a successful one. Test each element repetitively to produce the most optimized version of it even if they are a product of a successful campaign.

7. Failing to consider external factors

Tests should be run in comparable periods to produce meaningful results. It is wrong to compare website traffic on the days when it gets the highest traffic to the days when it witnesses the lowest traffic because of external factors such as sales, holidays, and so on. Because the comparison here is not made between likes, the chances of reaching an insignificant conclusion increase.

8. Using the wrong tools

With A/B testing gaining popularity, multiple low-cost tools have also come up. Not all of these tools are equally good. Some tools drastically slow down your site, while others are not closely integrated with necessary qualitative tools (heatmaps, session recordings, and so on), leading to data deterioration. A/B testing with such faulty tools can risk your test’s success from the start.

You may be interested in: A/B testing with Google Optimize

9. Sticking to plain vanilla A/B testing method

While most experienced optimizers recommend that you must start your experimentation journey by running small A/B tests on your website to get the hang of the entire process. But, in the long run, sticking to plain vanilla A/B testing methods won’t work wonders for your organization.

For instance, if you are planning to revamp one of your website’s pages entirely, you ought to make use of split testing. Meanwhile, if you wish to test a series of permutations of CTA buttons, their color, the text, and the image of your page’s banner, you must use multivariate testing.


We hope that the above analysis should give you a detailed overview of the common A/B testing mistakes – so that you may conduct the process properly. If you are looking for a software development agency to help with your website/app development project, don’t hesitate to reach out to JSLancer – our team will be more than happy to provide a FREE consultation on how we can help you visualize your goals.

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