This one took me a minute to get my footing.
Start by defining the experiment's goal and unit of randomization, then outline primary and secondary metrics with clear definitions. Address spillover by considering cluster randomization or measuring interference, and propose methods to isolate the treatment effect.
Pro tip: Use a switchback or cluster-randomized design to mitigate spillover, and pre-register your analysis plan to avoid p-hacking. Also, consider using a difference-in-differences approach if randomization is compromised.
Clearly state the hypothesis: surge-pricing push notifications increase driver supply at the airport. Specify the target population (e.g., drivers who frequently serve the airport) and the treatment (notification with surge pricing).
Decide whether to randomize at the driver level or use a cluster/switchback design to handle spillover. Consider geographic or temporal clustering if drivers interact.
Primary: number of drivers entering the airport geofence or completed trips from airport per unit time. Secondary: driver acceptance rate, time to accept, earnings per driver, and overall airport wait times.
Use cluster randomization (e.g., by city or time blocks) to minimize contamination. Alternatively, measure spillover by tracking control drivers' behavior and adjust using techniques like difference-in-differences or instrumental variables.
Conduct power analysis, run the experiment, and analyze with appropriate statistical tests. Check for novelty effects, seasonality, and ensure results are robust with sensitivity analyses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.