Problem
Scenario
You are evaluating an online experiment for a digital commerce product, and early results look promising. Before acting on them, you want to make sure the observed lift is not driven by common experimentation pitfalls such as early stopping, novelty, interference across users, or traffic allocation issues.
Question
What are some common pitfalls in online experiments, and how would you avoid them in the way you design, monitor, analyze, and interpret the test?
What This Tests
- Ability to name major online experimentation pitfalls
- Understanding of how those pitfalls bias estimates
- Use of guardrails and pre-registered decision rules
- Awareness of SRM, peeking, novelty, and interference
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