A/A testing entails exposing two or more individuals to identical versions of the same content. The objective is to detect no difference between the control and the variants, as opposed to finding an increase in conversions.
The concept behind an A/A test is that since the experience is identical for both groups, the expected KPI (Key Performance Indicator) will also be identical. So how is it different from A/B testing?
Difference Between A/A Testing and A/B Testing
A/B testing is used in A/A testing to compare two versions of a webpage that are entirely similar to one another. In most cases, this is done to verify that the instrument being utilized to carry out the experiment is statistically valid.
In the case of an A/A test, the tool must indicate that there is no difference in conversion rates between the control and variation for the test to be considered valid.
When conducting A/B testing, traffic is directed to two distinct pages — the control and the variation (another version of the original page) — to determine which version is more successful at converting visitors into customers.
In an A/A test, two identical pages are compared to one another. An A/B test’s objective is not to identify a boost in conversions but to validate the hypothesis that there is no discernible difference between the control and variation versions of a product.
Why Conduct A/A Tests With Same Test Pages?
Before conducting an A/B or multivariate test, it is recommended that you monitor on-page conversions on the page where the A/A test is being conducted. This will allow you to count the number of conversions and figure out the baseline conversion rate.
In the vast majority of other circumstances, the A/A test verifies the efficiency and precision of the A/B testing software. You need to check to see if the software says a statistically significant difference exists between the control and the variation (one with a statistical significance of more than 95 percent).
It is problematic if the program indicates a statistically significant difference between the two groups. You are going to want to make sure that the software is installed properly on your website as well as your mobile app.
Factors to Consider When Running A/A Tests
When doing an A/A test, it is essential to remember that it is always possible to identify a difference in conversion rate between the test page and the control page, even if both pages are similar.
When it comes to testing, there is always some degree of randomness involved; therefore, the fact that this is a negative reflection on the A/B testing platform is not always a bad thing.
It is essential to remember that the statistical significance of the data is not an absolute but rather a likelihood when conducting any A/B test. Even if the level of statistical significance is set at 95 percent, there is still a 1 in 20 possibility that the results you are observing are simply the consequence of random chance.
Your A/A test should, in most circumstances, conclude that the conversion improvement between the control and variation is statistically inconclusive. This is because the underlying fact is that there isn’t one to find, and the test is designed to reflect this reality.
How Exactly Is A/A Test Carried Out?
The process of doing an A/A test is quite similar to that of conducting an A/B test; however, in this instance, the two groups of users that are randomly chosen for each variation are provided with the same experience.
The rundown, in brief:
- Two groups are each given access to an almost identical version of a popular high-traffic website.
- Both of these groups will have a comparable experience when using the product.
It is anticipated that the KPI, which stands for key performance indicator, will be the same for both groups. If the KPIs do not correspond, one must investigate the factors that led to the unexpected result.
Note: You will also want to interface your AB testing tool with your analytics so that you can compare the conversions and revenue reported by the testing tool to the conversions and revenue reported by analytics – they should correlate.
Advantages of Performing A/A Tests
It’s not hard to picture what going through an A/B test might be like. Nevertheless, most teams discover that the reality is almost always more complex and nuanced than the plan.
The following is a list of the most common and useful information that you can obtain from an A/A test:
- Who is accountable for the step(s) they are responsible for in the process? What kinds of modifications to the underlying code are required for various changes?
- Is the tracking of all the events that are important to you genuinely accurate? What does it look like when a test result is falsely positive, and how can we tell if a result can be trusted?
- How much time does it take to reach a certain number of users, say N?
- How much time is required to plan an A/B test, carry it out, and then wrap it up?
In addition to these very significant takeaways, teams typically find that running through a dress rehearsal helps them educate themselves in a very low-risk manner with the new tools, the new way to view the data, and the new procedures.
Disadvantages of A/A Testing
The issue constantly consumes both test time and real traffic, necessitating preloading the test runtime with an interval of A/A testing in order to work around it.
So if you try to run forty tests every month, your capacity to get things live will be severely compromised by this. To validate the results of the experiment, we think it would be more worthwhile to spend a half day on quality assurance testing than do A/A testing for two to four weeks.
The second issue is that the significance level of approximately 80 percent of A/A tests will be reached at some point in time. In other words, the evaluation system will definitely conclude that the original is superior to the original with a very high degree of certainty!
Why? It has something to do with the numbers and the sampling, but it’s also because the test is interpreted incorrectly. If you only have a few examples, there is a good chance that you will wrongly assume that something is flawed even though the problem does not exist.
When you undertake A/B testing, you are comparing the effectiveness of two things that are the same in every way. In contrast to an A/B test, proving that there is no substantial bias requires a significantly larger number of samples and pieces of data to be analyzed.
It is quite difficult to identify marginal gains, and when you test similar things, this difficulty becomes even more evident. For this reason, when we conduct split tests, we avoid testing things that are very similar. You could run an A/A test for several weeks LONGER than the actual A/B test itself, but you still wouldn’t gain any significant information on whether or not the test was tampered with or how well you understand sampling.
To Wrap Up
A/A testing is a type of multivariate experimentally-informed research. It uses randomized controlled trials (often in a lab setting) and quantitative analytics to study whether or not one intervention helps or harms the outcome of another.
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