A/B Testing
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A/B testing vs. split testing: Which should you use?
Discover how A/B testing and split testing differ from one another, and learn how they can help optimize your website or app and drive conversions.
Okay, let’s be honest here — for 95% of people, they mean the exact same thing when they say “A/B testing” and “split testing.”
Both involve randomly splitting users between two or more variations of an experience (an A and B version) to see which performs best.
In the A/B testing term, the “A” usually refers to a control variation, which represents no changes at all vs. the current status quo. This may not always be the case, though.
When testing email marketing, for example, you may only be sending a message to your list once. Hence, there is no “status quo” to test against. The “A” and “B” variations are both technically new potentials. Sometimes, this is where you hear the term “split testing” used more often.
If you’re searching for the difference between A/B testing and split testing, though, we’ll assume that you’re hearing the terms used somewhere for different use cases.
If that’s your problem, we’re here to help:
Let’s compare A/B testing vs. split testing in how each term is commonly used to see if we can uncover any real differences.
We’ll cover:
- A definition of A/B testing
- Why A/B testing and split testing are essentially the same thing
- How splitting traffic enables A/B testing
- A quick rundown of other testing methods you can use
- Frequently asked questions about A/B testing and split testing
Let’s begin.
What is A/B testing?
A/B testing is an experimentation technique for figuring out which version of your website or app works best. You create two versions (let's call them A and B) and show each to different users.
The important part is that with A/B testing, we usually change only one thing at a time between versions A and B.
This lets us clearly see if that one change makes a difference in how people act on our site. Are they clicking more? Buying more? Staying longer? A/B testing helps answer those questions by giving us real data instead of just guessing.
Example: Button color in an e-commerce site
Let's imagine you have an online store. You want to see if changing the "Add to Cart" button from blue to green will encourage more people to buy items. This is a perfect situation for an A/B test:
- Version A (control): Your current page with the blue button.
- Version B (variation): The exact same page, but with a green button.
By tracking how many people add items to their cart on each version, you can determine if the green button actually leads to more sales. This gives you a solid basis for deciding which button color is better for your business goals.
A clarification on A/B testing vs. split testing
You might be wondering: What's the difference between A/B testing and split testing then?
As mentioned in the introduction (and if we want to get really granular with the definition), there's a tiny nuance:
Some people use "split testing" to refer to the broader idea of testing different versions by splitting traffic, while "A/B testing" specifically emphasizes the comparison of two versions (A and B). Again, with split testing, there’s really no “control version” to test against.
However, in practice, the terms are often used interchangeably, and the core concept remains the same: Testing different variations to see which one performs better.
So, don't get too hung up on the terminology. What’s really important is how this idea of splitting traffic works and how it enables you to make data-driven decisions to improve your website or app through A/B testing.
This leads us to the next question:
How does splitting traffic enable A/B testing?
The real magic behind A/B testing lies in how it allows you to divide your audience into different groups. Let's break down how this works:
Random assignment
When you run an A/B test, you're essentially splitting your incoming traffic into two groups. This is done randomly, meaning each visitor has an equal chance of being assigned to version A or version B of your webpage or app.
Why random assignment is so important
Random assignment is crucial because it helps ensure that the groups are comparable. This means that, on average, both groups should have a similar mix of ages, genders, interests, and other characteristics.
By keeping the groups as similar as possible, you can be more confident that any differences in performance are due to the changes you made between the versions, not some other hidden factor.
The power of controlled experiments
This random assignment creates a controlled experiment. You have two nearly identical groups of people experiencing slightly different versions of your website or app.
This allows you to isolate the impact of the specific change you're testing. If one version performs significantly better than the other, you can attribute that difference to the change you made, whether it's a new button color or a redesigned layout.
In the end, it's all about the data
By tracking the behavior of each group — how often they click, buy, or engage with your content— you collect valuable data. This data allows you to compare the performance of each version and determine which one is more effective in achieving your goals.
Let’s reiterate so it’s crystal clear: Though we technically could say A/B testing and split testing are slightly different practices, splitting traffic is just part of A/B testing, not a separate thing.
Splitting traffic creates a fair and controlled environment, letting you see the impact of specific changes and make data-driven decisions to improve your website or app's performance.
What other kinds of testing are there?
We've seen how A/B testing and split testing are basically the same thing; they just have different names for comparing multiple versions of a page or app to see which performs better.
But these aren't the only tools in your testing arsenal. There are a few other types of tests you might encounter:
- Multivariate testing: Think of this as taking your A/B testing up a notch. Instead of testing one change at a time, you test multiple variations of multiple elements simultaneously.
This approach gives you a more nuanced understanding of how different elements interact and can uncover hidden opportunities for optimization.
- Multipage testing: Sometimes, the changes you make on one page can ripple through your website, affecting how visitors interact with other pages.
Multipage testing lets you analyze the impact of changes across an entire user journey (like a checkout process or sign-up flow). This helps you understand how different pages influence each other and optimize the overall user experience.
- A/B/n testing: This is a more general term that encompasses A/B testing. The difference is that “n” represents the number of versions you're testing.
It's like having a competition between all your design ideas, where the best one wins. A/B/n testing is particularly useful when you have many different options to consider and want to efficiently narrow down the most effective one.
Frequently asked questions
What’s split testing?
Split testing means splitting traffic to your app or website to compare two or more versions of a change you’ve made to see which one performs better,
You then measure key metrics like clicks, conversions, or engagement to draw conclusions. It’s often used as a synonym for A/B testing.
What’s the difference between A/B testing and split testing?
While often used interchangeably, some make a subtle distinction. A/B testing refers to testing changes within the same overall design: “A” would be the control variation. This means there’s a “status quo” you’re testing against.
Split testing can refer to either the general concept of testing by splitting traffic or testing entirely different versions of a page or app. You’re essentially comparing two completely new potentials.
Next steps
By now, the difference (though slight) between A/B testing vs. split testing should be much clearer to see. But now the real question becomes how can you carry out these tests while making sure there are no hidden biases and that the data is trustworthy?
That’s when you should consider using Eppo.
Eppo is an end-to-end data warehouse-native experimentation platform that allows you to run A/B tests with an unprecedented level of rigor and accuracy.
Here’s how:
- Bullseye accuracy: Since Eppo is data-warehouse native, you can be 100% sure that you’re pulling data from your internal source of truth instead of using external tacked-on tools. Unreliable results become a non-issue thanks to Eppo’s focus on statistical rigor.
- Ease of use for everyone: Running A/B tests is a breeze with Eppo’s user-friendly interface and workflows based on SQL. This means running these tests will be much less taxing for your data teams (and non-technical users too).
- Experimentation that’s second to none: Eppo has features like sample size calculators, feature flagging, advanced diagnostics, and an extensive knowledge base to get you testing in just a couple of minutes.
- Insights you can actually apply: Eppo continually monitors your results, offering a solid understanding of your experiment outcomes and the effectiveness of your A/B tests
Ready to start experimenting and running your own A/B tests with total confidence?
Discover how A/B testing and split testing differ from one another, and learn how they can help optimize your website or app and drive conversions.
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