What Is A/B Testing?

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9 Dec 2022 13 min read

Companies want to know how a new feature or product will impact their overall user experience before they invest time and money to launch it. So, product managers use experimentation tools like A/B testing to gauge how a subset of the general public interacts with the changes they want to implement.

In this article, we explain what A/B testing is, how it works, and why you should use it, as well as common mistakes to avoid.

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Table of Contents

What Is A/B Testing?

A/B testing—often called split testing, multivariate testing, or hypothesis testing—is а tool that allows companies to understand whether a product feature or design change they want to implement is actually an improvement. In other words, it’s a controlled experiment to determine how a change in experience affects dependent metrics.

Simply put, it is a tool that helps you make reliable decisions based on data and improve business performance.

Split testing is used in many different technical areas, from software development to data engineering, statistics, product management, and design. Having said that, this tool isn’t exclusively reserved for tech products—it can be applied to a variety of domains. We’ll expand on this later in the article.

But first, let’s learn more about its origins and how it works.

When Did A/B Testing Originate?

A/B testing comes from a domain with much slower development pipelines and a much slower release cadence than its current dynamic nature in the digital realm. Actually, this concept was implemented long before digital products even existed.

Ronald Fisher is one of the first people to conduct a variation of what we now know as split testing in the 1920s. As Kaiser Fung says in the Harvard Business Review, “He wasn’t the first to run an experiment like this, but he was the first to figure out the basic principles and mathematics and make them a science.”

How did he do it?

Fisher raised agricultural questions on the effects external factors had on crops, dividing them into samples to assess how the plants would grow when infused with different types of fertilizer.

From then until modern times, A/B tests have evolved significantly. This concept has now become the norm for companies to derive quantifiable insights into their customer base’s preferences.

How Does A/B Testing Work?

As we’ve already mentioned, A/B testing is a research methodology that allows you to measure the impact of a potential change in your product based on a sample of your data.

Typically, a product manager or owner will run a test on this sample to see how users react to proposed changes to the product. Based on the statistical results, they will decide whether a new feature is beneficial to the company and to the consumers.

The person running the A/B test will select a part of all users—for example, one percent if it’s a sufficiently big sample. They will further divide it into a control group with a default user experience and at least one test group with a new experience that want to test.

Once the experiment has finished running, management will ask their data scientists whether they can trust the results. If they are statistically significant, the test is reliable. This gives leaders the confidence that if they release this product, the rest of the user base will have the same experience with this feature as the test sample.

If product owners obtain the desired outcome and it is statistically significant, then they can successfully release the feature. Otherwise, the the company can cancel or postpone the launch and continue testing until they find a successful formula.

In essence, A/B testing answers questions such as:

  • Which metrics or user behavior change?
  • How much do they change?
  • Is this change reliable and trustworthy?

That being said, the tool does not reveal precisely why the change occurred or what users like about it. These questions are better answered by working together with user researchers and qualitative research methodologies.

A/B testing isn’t necessarily a panacea. And it’s a good idea to not overdo it—only run one test at a time on a single feature. Additionally, make sure you have a clear idea of how you’ll measure the success factors and for how long. We’ll explain in detail how to do that below.

To summarize, the key characteristics of an A/B test are that it is:

  • A controlled deliberate experiment
  • Based on a randomized set of users
  • A defined hypothesis and ability to measure success

What Are Examples of A/B Testing?

A/B testing has applications in multiple domains. It’s commonly used to test digital products where it’s easier to introduce changes like a new application build or a website revamp. Digital product managers and marketing analysts benefit from this tool immensely as it allows them to start testing these changes on users immediately, tracking the results in real-time.

There are plenty of examples of companies doing A/B testing on their products. If you own a smartphone or receive e-mails, you’ve most likely been part of hundreds of split tests—if not more. Chances are that your experience on social media like Facebook or Instagram differs substantially from that of a person sitting next to you on the subway scrolling through the same application at the same time.

From Google trying out different shades of blue for their CTA buttons to Facebook changing the arrangement of its feed, these decisions are all made through A/B testing. The scale to which you can apply this type of experiment is incredibly broad, and it doesn’t have to be on just digital products either.

Some of its use cases apply to fields such as:

  • Marketing
  • Social studies
  • Government policies
  • Clinical trials

The most notable use case is in the medical field. If you’ve ever opened a statistics textbook to a chapter on hypothesis testing, you’ve most likely read what an A/B test is in a nutshell.

The most common examples are tests of a new type of treatment for patients with a specific disease or sickness. In such cases, you would compare the new treatment to the base treatment or even to no treatment at all, based on pre-defined metrics. Over time, you’ll see whether your patients are getting better with this new treatment.

Using statistical methodologies, you’ll understand whether you can trust the results of this experiment. From there, you can derive conclusions on whether other patients affected by the same kind of disease would react to the treatment like your test base.

Actually, the word treatment comes from medicine, yet it’s still used daily in the context of digital products even though they do not treat anyone.

How to Perform A/B Testing?

A/B tests are quite useful in the digital age and have been made incredibly accessible. Depending on what you want to test, there are several approaches you can take.

In most digital companies that work with agile methodologies, you will have a feature development team responsible for the product, a part of the product, or a specific feature that they’re building. Some of the team members include:

  • A product/project manager
  • Software developers
  • An engineering manager
  • UX/UI designers
  • Data engineers or data scientists

Each member of the team handles a different step in the A/B testing process.

However, a split test can be done on a much smaller scale. You can use it to:

  • Implement a new feature on your website
  • Test the effectiveness of your CTA button
  • Optimize your webpage copy

There’s a variety of tools and software available online that lets you experiment and analyze results automatically. Google Optimize, Optimizely, VWO, and Zoho PageSense are great options depending on your budget.

Regardless of the tool you use, however, the main steps to run a successful A/B split test are the same.

Step 1: Formulate a Hypothesis

First and foremost, you must know what you want the A/B test to reveal. A hypothesis is an assumption on how the proposed change will affect the users. The goal is to either support or reject the hypothesis by running an experiment on one control and one test group.

In a business setting, the project or product manager is most often responsible for this step. They will work with a data scientist on the team until they reach a specific testable hypothesis.

Step 2: Create Variations

Now, it’s time to create whatever you’re going to A/B test—for example, changing the color of your CTA buttons from blue to green. In this case, you’ll need only two variations of the button: blue and green.

Generally, it’s bad practice to have too many variations as they can slow down your processing time, confound the results, and mess with your final analysis. So, keep them to a minimum.

Step 3: Define the Success Metrics

One of the most important steps in an A/B test is to decide exactly how you’ll track the results. Continuing with the CTA example from the previous step, your metric can be based on the percent increase or decrease in the button’s click rate depending on the color.

Be sure to allocate one metric per experiment. If you have too many, you would not be able to trace where the results came from.

Step 4: Split the Sample

Here, you decide who to target with the changes in your product and how large the sample would be. The control and test groups should be randomized and equal in size so as to give you a level playing field and reduce bias.

Make sure you’ve chosen a big enough sample size for the nature of your A/B testing purposes. Otherwise, the test won’t be reliable.

Step 5: Determine the Parameters of the A/B Test

This step includes answering questions like:

  • How long to run the A/B test for?
  • How to monitor the performance?

It’s important to define a specific timeframe for your experiment and accommodate for other factors that might sway your results, such as fluctuating consumer behavior. Allow a sufficient amount of time for your A/B test to run its course. This will depend on the size of your sample and the scale of your business.

Often, this step is done by data scientists. However, it can be done in conjunction with data engineers, software developers, and potentially the project manager.

Step 6: Run the Test

This step is as straightforward as it sounds. After you’ve narrowed down your hypothesis, metrics, and parameters, the only thing left is to go live.

It’s important to note here that both variations A and B of your product must be launched and tested simultaneously to achieve optimal results.

Step 7: Analyze the Results

The final step is to analyze the results. If everything went smoothly, that is.

As a data scientist, this is where your analytics training comes into play. Some things to look out for include:

Based on this analysis, the project manager in your company would often decide whether to iterate on this test, release the product to all users, or scrape the test entirely and start from somewhere else.

If you’re conducting the split test by yourself using a software tool, then often this analysis will be done for you. However, keep your eyes peeled for not-so-obvious metrics or trends that might have affected the A/B testing results.

What Are Some Common A/B Testing Mistakes?

Not all experiments bring conclusive results. Sometimes, it’s because the user base simply has no clear preference. In that case, there’s not much you can do.

However, your own A/B testing strategy can also sabotage your results if it’s biased or flawed. Here, we’ll outline four common mistakes that you should avoid.

Testing Without Setting Parameters

Many people make the mistake of running an experiment without giving it much thought, which leads to inconclusive or meaningless results. Defining parameters include having a valid hypothesis, trackable metrics, and a set timeframe before you begin.

Testing With Too Many Variations

As we mentioned previously, too many variations can confound the results and you won’t know where they are coming from. One of the best A/B testing practices is to test only two variants of the same element or feature at a time. This will eliminate the guesswork when you’re interpreting the results. After you’re done, you can always run another experiment.

Ending the A/B Test Too Soon

Some data scientists or even digital marketers may see a significant result a few days into their A/B split test and hastily stop the process. That’s very bad practice. You need a good quantity of data for a reliable experiment. If you end your test too early, you risk damaging your conversion rates and product impact.

Letting the A/B Test Run Too Long

Just as it’s not optimal to end a test too early, you don’t want to leave it running for too long either. Consumer behavior fluctuates, influenced by a variety of external factors, which can affect the course of your experiment. Not to mention that you can fall prey to cookie deletion as most users would clear theirs every few weeks and may land upon a different A/B variation.

Be sure to avoid these mistakes and follow the A/B testing best practices, and you’ll be one step closer to designing a product your customers will love.


What Is A/B Testing?
A/B testing, otherwise known as split testing or multivariate testing, is a controlled research method that measures the impact of a new treatment on a given product. This tool has been used to experiment with different variables in agriculture, clinical trials, and even government policies. However, in the rapidly advancing digital age, A/B tests are used more on digital products and in marketing. How does it work? You split a randomized sample set of users into two groups: control and testing. Each group will see a variation of what you’d like to test—control users will see the original product, whereas test users are exposed to the new feature. Product managers, data scientists, or marketers monitor the performance of each set over time. Once the experiment finishes its run, someone—often a data scientist—would analyze the results based on pre-defined metrics, determine if the data is reliable, and help management decide whether to implement the changes based on the A/B test’s results.


What Is the Goal of an A/B Test?
The goal of an A/B test is to provide insight into the more optimal version of a product or feature. Put simply, it aims to find the best results for a given metric. For example, imagine that you had to choose between two university majors: data science and medicine. In an ideal world, you’d be able to clone yourself and send your doubles to study either course. As the clones complete their degrees, you’d have monitored their performance. At the end of the experiment, you’d go back in time to apply for the major that yielded the best results. That could be Clone A landing a data science job at a large company or Clone B getting accepted into a fast-track pre-med program. Data scientists use this tool as a scalable solution to decide which proposed variation will have better conversion rates based on the users’ experience. You will find a use for it in marketing, design, and digital product development.


What Is an Example of A/B Testing?
One notable example of A/B testing involves website landing pages. Companies often run experiments with different variants of a landing page to see which could yield better conversion rates and more sales. From the webpage copy to the design elements, marketers change different elements to see which will ‘click’ with their audience best and drive them to make a purchase. The variation can be as little as a another call-to-action, yet still produce a significant difference. In this way, businesses—and especially marketers—get to know their consumer base much better. Analyzing the results produces valuable insights into user behavior that they can later turn into an actionable strategy.


A/B Testing: Next Steps

As an aspiring data scientist, you must understand what A/B testing is and how to apply it to achieve quantifiable results. This is an integral part of business development. In fact, knowing where and how exactly you can implement this type of experiment can help you stand out on the job market.

You can further elevate your data science skills with our A/B Testing in Python course where you’ll learn the mechanics and how to apply them in a real-life business environment. Take a step toward data science success. Sign up now via the link below to access a selection of free lessons.