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Statistics & Math questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master statistics & math interviews.

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This is a medium difficulty Statistics & Math question, commonly asked during Technical Screen rounds at Uber.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Uber during technical interviews.

Analyze Cancellation Change with Statistics

Quick Overview

This question evaluates statistical inference for binary outcomes, regression adjustment for confounding, experiment design and power analysis, and nonparametric uncertainty estimation using bootstrapping, and is targeted at Data Scientist roles within the Statistics & Math domain.

A/B change in cancellation rate (before vs after)

Context: You are evaluating a small product tweak intended to reduce cancellations. Treat each trip request as a Bernoulli outcome (1 = canceled, 0 = not canceled) to start.

Observed data:

Before: n1 = 50,000 requested trips, cancel rate p1 = 7.2%.
After: n2 = 48,000 requested trips, cancel rate p2 = 6.6%.

Tasks:

Hypothesis test and CI
- Choose an appropriate test for the change in cancellation rate. State H0 and HA.
- Compute a 95% confidence interval for the absolute rate difference Δ = (p_after − p_before) and interpret it in practical terms.
Adjustment for confounding (city, hour-of-day)
- Specify a logistic regression that adjusts for city and hour-of-day, and include one interaction you deem important.
- Write the model formula, list key assumptions, and describe how you would check calibration and overdispersion.
Power and MDE
- What per-arm sample size is required to detect an absolute change of 0.5 percentage points at α = 0.05 with 80% power (ignore clustering first)?
- Then discuss how an intraclass correlation (ICC) of 0.02 at the driver level inflates the required N under a cluster-robust design. Show the design effect and the new N.
Nonparametric check (bootstrap)
- Describe a bootstrap procedure to form a CI for the rate difference, stratified by city (stratified resampling).
- Explain when this CI might be preferable to the z-approximation.

Quick Overview

A/B change in cancellation rate (before vs after)

Context: You are evaluating a small product tweak intended to reduce cancellations. Treat each trip request as a Bernoulli outcome (1 = canceled, 0 = not canceled) to start.

Observed data:

Before: n1 = 50,000 requested trips, cancel rate p1 = 7.2%.

After: n2 = 48,000 requested trips, cancel rate p2 = 6.6%.

Tasks:

Hypothesis test and CI

Choose an appropriate test for the change in cancellation rate. State H0 and HA.
Compute a 95% confidence interval for the absolute rate difference Δ = (p_after − p_before) and interpret it in practical terms.

Adjustment for confounding (city, hour-of-day)

Specify a logistic regression that adjusts for city and hour-of-day, and include one interaction you deem important.
Write the model formula, list key assumptions, and describe how you would check calibration and overdispersion.

Power and MDE

What per-arm sample size is required to detect an absolute change of 0.5 percentage points at α = 0.05 with 80% power (ignore clustering first)?
Then discuss how an intraclass correlation (ICC) of 0.02 at the driver level inflates the required N under a cluster-robust design. Show the design effect and the new N.

Nonparametric check (bootstrap)

Describe a bootstrap procedure to form a CI for the rate difference, stratified by city (stratified resampling).
Explain when this CI might be preferable to the z-approximation.

Analyze Cancellation Change with Statistics

Quick Overview

A/B change in cancellation rate (before vs after)

Solution

Comments (0)

Analyze Cancellation Change with Statistics

Quick Overview

A/B change in cancellation rate (before vs after)

Solution

Comments (0)