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Evaluate Email Subject Line Performance Using Hypotheses

Last updated: Mar 29, 2026

Quick Overview

This question evaluates proficiency with hypothesis testing for proportions, application of the Central Limit Theorem, and power/sample-size calculations in the context of A/B testing email click-through rates.

  • medium
  • Uber
  • Statistics & Math
  • Data Scientist

Evaluate Email Subject Line Performance Using Hypotheses

Company: Uber

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Email marketing team wants to evaluate the performance of a new subject line. ##### Question Define the null and alternative hypotheses for comparing click-through rates between control and test emails. Explain how the Central Limit Theorem justifies using a z-test in large samples. Derive the sample size needed to detect a 2-percentage-point lift with 80% power at α = 0.05. ##### Hints Think proportions, pooled variance, power formula.

Quick Answer: This question evaluates proficiency with hypothesis testing for proportions, application of the Central Limit Theorem, and power/sample-size calculations in the context of A/B testing email click-through rates.

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Uber
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Statistics & Math
15
0

A/B Test of Email Subject Lines: CTR Hypotheses, CLT Justification, and Sample Size

Context

You are comparing click-through rates (CTRs) between a control email (current subject line) and a test email (new subject line). Each recipient either clicks (1) or not (0), so CTR is a proportion. Assume independent users and equal allocation between variants.

Let:

  • p_c = CTR of control
  • p_t = CTR of test
  • n_c, n_t = sample sizes for control and test (often n_c = n_t = n)
  • α = 0.05 significance level, power = 0.80 (β = 0.20)
  • Desired minimum detectable effect (MDE) = 2 percentage points = 0.02

Tasks

  1. State the null and alternative hypotheses for comparing CTRs.
  2. Explain how the Central Limit Theorem (CLT) justifies a z-test for large samples.
  3. Derive the sample size needed per group to detect a 2-percentage-point lift with 80% power at α = 0.05. Provide a general formula in terms of the baseline CTR and a numeric example.

Solution

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