Stop 10 · A/B Testing & ExperimentationTopics · 7/8
Segmentation in Experiments
Part of the Data Analyst Roadmap
In short
Checking whether an experiment's effect differs across user segments (new vs returning, mobile vs desktop) — a result that looks flat overall can hide a strong effect in one segment.
How to learn this
- 1Break down a hypothetical test result by 2-3 plausible segments.
- 2Learn why segment-level findings need larger samples to be trustworthy (multiple comparisons).
- 3Practice deciding which segment cuts are worth checking before you run the test, not after.
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