This question evaluates a data scientist's experimental design and causal inference skills, specifically A/B testing methodology, metric selection, power analysis, interference handling, bias detection, and product analytics for trust-and-safety features.
You are a data scientist at a financial or crypto platform. The product team wants to launch an Identity & Trust feature that adds stronger identity verification and a visible trust badge in the transaction flow. The hypothesis is that the feature will reduce fraud, scams, and disputes, while increasing the rate of successful transactions. However, it may also add onboarding friction and hurt conversion.
Design an A/B test to evaluate this feature. In your answer, address: