More feature flags related terms

A/A testing


A/A testing is a method used to verify the accuracy and reliability of A/B testing tools and processes. In A/A testing, the same version of a webpage or app is presented as both the "control" and the "variant" to two similar audiences. Since both groups are exposed to the exact same content, any differences in metrics should theoretically be negligible, highlighting the natural variability in data and ensuring the A/B testing setup is statistically sound.


The primary purpose of A/A testing is to check the statistical validity of A/B testing processes. It helps identify any flaws in the testing environment, such as improper segmentation, biased sample selection, or technical glitches that could skew the results of future A/B tests.


  1. Setup Identical Versions: Both the control group and the variant group are shown the same version of the content.
  2. Randomized Exposure: Users are randomly assigned to either the control or variant group to ensure unbiased distribution.
  3. Data Collection and Analysis: Metrics such as conversion rates, click-through rates, and engagement levels are collected and analyzed. The expectation is that there will be no significant difference between the two groups.

Key Benefits

Best Practices


A/A testing is a foundational step in the optimization process, serving as a diagnostic tool to validate the effectiveness and accuracy of A/B testing setups. By incorporating A/A testing into their optimization strategy, organizations can enhance the reliability of their testing processes, leading to more accurate and actionable insights from subsequent A/B tests.

A/B testing

Comparing two versions of a web page or app against each other to determine which one performs better.

Learn about A/B testing


Bayesian methodology updates beliefs based on prior knowledge and new evidence, offering flexibility and clear interpretability. It's valuable for understanding uncertainty and predicting events in fields like machine learning and decision-making.

Learn about Bayesian

User segmentation

Dividing users into groups based on behavior or attributes for targeted feature releases.

Learn about User segmentation

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