A/A Testing

A/A testing is the process of comparing two pages with the same content and elements against each other. It is like A/B testing, wherein an audience is split into groups to test various parts of a page or a campaign. However, there are no variations on the samples, and the expected key performance indicators are the same for each group. If the testing is done right, the results will not report any differences between the control and the variations.

A/A testing is done mainly to determine the reliability and the statistical fairness of A/B testing. Without it, marketers run the risk of interpreting false positives and making inaccurate assumptions about what works for a page or a campaign.

Screen capture of Lord & Taylor homepage.

Why It Matters

A/A testing is important in identifying what works and what does not in a page or a campaign. You can also set the baseline conversion rate through A/A testing before conducting other tests.

When it comes to A/B testing, A/A testing can check the effectiveness and accuracy of a testing tool. There should be no statistically significant difference between control and variation groups.

Testing identical pages and campaigns can also help you decide on what the appropriate sample size should be. When the sample size is too small, you can have the problem of discounting small but significant segments that can impact results.

Challenges of A/A Testing

A/A testing has no control over the randomness that occurs in an experiment. The results can show a variation of statistical significance without it being founded in certainty. It can imply that the conversion rate between samples is probabilistic.

The testing can also require a large sample size, and is, therefore, time-consuming. Large sample size is required when conducting A/A testing to determine if the first sample is indeed preferred over the other version. The data and sample necessary to prove that there is no significant difference are larger than when conducting other experiments.

Types of A/A tests

  • Hypothesis Testing. You will need to determine a sample size beforehand. Eventually, you will gain enough samples to determine if there is a significant difference in your KPIs among groups.
  • Bayesian Testing. This kind of testing is sensitive to changes with every data collected. A variation is considered better even when there is a tiny difference in the results. There is no need for pre-determined sample size, unlike with hypothesis testing.

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