What are common segmentation biases in testing?

Survivorship bias occurs when you only analyze segments that remained active, ignoring those who unsubscribed or churned. This makes your remaining audience look more engaged than it actually was, distorting your understanding of what works.

Selection bias happens when segment assignment correlates with the outcome you're measuring. If your most engaged subscribers end up in one test group because of how you defined it, the test results reflect that pre existing engagement rather than the treatment effect.

Sample size issues plague small segments. With too few subscribers in a test group, random variation dominates the results. What looks like a winning strategy might just be noise. Always calculate statistical significance before drawing conclusions.

Timing bias affects tests that run during unusual periods. A segment that performs well during a holiday sale might underperform during regular periods. Test across multiple time windows to separate treatment effects from seasonal noise.

Recency bias leads marketers to overweight recent behavior when defining segments. Subscribers who just took an action look different from those who took the same action months ago, but the segment definition treats them identically.

Bias is the hidden current that pushes your ship off course without you noticing. Acknowledge it, measure it, and correct for it.