A/B Testing
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
Randomly divide users into two groups: one uses the old version (A), the other uses the new version (B), and determine which version is better by comparing data. This is the gold standard for causal inference.
SCAFFOLDING EFFECT
Reduce cognitive load
Use data to counter arguments. When the team argues endlessly over 'red button or green button', stop arguing and run an A/B test. Let reality (data) be the judge, not the person with the highest position.
Anchor fast decisions
Based on 'controlled experiments' and 'causal inference'. Random grouping controls confounding variables, so differences can be attributed to the version itself, replacing subjective arguments with data.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/A/B_testingverified
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