You’re a data scientist on the Autopilot/Vehicle Dynamics team at VoltRide, an EV manufacturer with ~800k connected vehicles in North America. A new over-the-air (OTA) software update changes the brake blending algorithm (regen + friction) and is expected to make deceleration feel smoother, reducing customer complaints and improving safety perception.
The team runs a randomized controlled experiment: eligible vehicles are randomly assigned to Control (old braking logic) or Treatment (new update). For each vehicle, telemetry is aggregated over a 7-day window after assignment. The primary metric is braking jerk during moderate braking events (units: m/s³). Lower jerk indicates smoother braking.
Because jerk has occasional spikes (potholes, emergency braking), the metric is defined as the 95th percentile jerk per vehicle over qualifying events (e.g., deceleration between 0.8–2.5 m/s², speed 10–80 km/h, dry road inferred from sensors).
Determine whether the OTA update improved braking smoothness, defined as a reduction in mean per-vehicle 95th-percentile jerk, and quantify the size of the improvement.
Assume per-vehicle 95th-percentile jerk values are approximately independent across vehicles. Summary statistics from the experiment are:
| Group | Vehicles (n) | Mean jerk (m/s³) | Std dev (m/s³) |
|---|---|---|---|
| Control | 1,842 | 1.972 | 0.612 |
| Treatment | 1,799 | 1.901 | 0.598 |
Additional info: