Evaluate Smart Wait Launch Impact

An autonomous-vehicle or ride-hailing product introduces a feature named Smart Wait. Before a rider confirms a trip, Smart Wait presents a more conservative wait-time estimate, so the displayed figure tends to be longer than the previous one. The intent is to make ETAs more accurate and to reduce frustration caused by estimates that were too optimistic.

After launch, the team sees:

• The quote-to-ride conversion rate fell by 5% relative.
• Actual time to pickup (TTP) dropped by 25% among completed rides.

A product manager argues: "Fleet efficiency got better because actual TTP went down."

Answer these questions:

1. Do you accept the PM's conclusion? Why or why not?
2. What other hypotheses might explain the drop in TTP, particularly given the conversion decline?
3. Which metrics and analyses would you use to judge whether Smart Wait was beneficial overall?
4. If you were designing an experiment for this feature, what would you randomize, what would be the primary metric, and which guardrails would you track?

Overview: This question tests a data scientist's skills in causal inference and experiment analysis, including reading A/B test results, weighing conversion against operational metrics, choosing primary and guardrail metrics, and forming alternative hypotheses.