A Feed change spreads between friends — is your A/B test lying?
Experiment designHardProblem. You're testing a Feed change designed to increase resharing. You randomize users 50/50 and measure reshares. A colleague says the result will be biased. Why might they be right, and how would you design the test properly?
Before you reveal: say your answer out loud, as if you were in the real interview — get your reasoning across clearly first. There is no single correct answer: reading what the interviewer is really after and defending your own thinking is what makes an answer strong.
Strong-answer signal: naming the SUTVA violation, getting the bias direction (toward zero for viral features), switching to cluster randomization with an inverse-probability estimator, and pricing the power cost. Common trap: knowing "network effects are bad for A/B tests" but not the bias direction or the concrete fix.