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Interpret p=0.10 rigorously

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of hypothesis testing, confidence intervals, power calculations, p-value interpretation, and variance-reduction techniques within A/B testing and experimental design for a Data Scientist role.

  • medium
  • Other
  • Statistics & Math
  • Data Scientist

Interpret p=0.10 rigorously

Company: Other

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Technical Screen

An A/B test aims to reduce monthly churn among subscribers. Over 28 days you randomize 150,000 users per arm. Control churn=5.00%; treatment churn=4.85%. A two-sided test yields p=0.10 at α=0.05. Answer: (1) compute the 95% confidence interval for the absolute churn difference (treatment minus control, in percentage points) and interpret it; (2) compute post-hoc power to detect a −0.15 pp effect with these sample sizes; (3) explain why p=0.10 is not “a 10% chance the null is true,” and state what it does mean; (4) under what pre-registered conditions would a one-sided test be appropriate here, what would its p-value be approximately, and why is switching to one-sided post hoc invalid; (5) name two design/analysis changes (e.g., covariate adjustment, stratification, CUPED) that could legitimately reduce variance and potentially move p below 0.05, and when they are valid.

Quick Answer: This question evaluates understanding of hypothesis testing, confidence intervals, power calculations, p-value interpretation, and variance-reduction techniques within A/B testing and experimental design for a Data Scientist role.

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Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Statistics & Math
1
0

A/B Test on Monthly Churn: Inference, Power, and Testing Choices

You ran a 28-day A/B test to reduce monthly churn among subscribers, randomizing 150,000 users per arm. The observed churn rates were:

  • Control: 5.00%
  • Treatment: 4.85%

A pre-specified two-sided test reported p = 0.10 at α = 0.05.

Tasks:

  1. Compute the 95% confidence interval for the absolute churn difference (treatment − control, in percentage points) and interpret it.
  2. Compute the post-hoc power to detect a −0.15 pp effect with these sample sizes.
  3. Explain why p = 0.10 is not “a 10% chance the null is true,” and state what it does mean.
  4. State when a one-sided test would be appropriate if pre-registered, approximate its p-value here, and explain why switching to one-sided post hoc is invalid.
  5. Name two design/analysis changes (e.g., covariate adjustment, stratification, CUPED) that could legitimately reduce variance and potentially move p below 0.05, and when they are valid.

Solution

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