Explain Simpson's Paradox with a user engagement example
This tests your understanding of statistical pitfalls in A/B testing. A good answer defines the paradox, gives an example where a feature fails in aggregate but wins in every segment, and attributes it to a confounding variable.
This tests your ability to spot confounding variables and avoid misinterpreting A/B test results. A strong answer defines Simpson's Paradox, then constructs a scenario where a feature looks bad overall but is actually successful for both new and returning users. The key is showing how a lopsided segment mix (e.g., mostly new users) distorts the aggregate metric. A red flag is confusing the paradox with simple statistical noise or failing to identify the confounding variable.
Read the original → statsig.com
- #analytics
- #a/b testing
- #statistics
- #data interpretation
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