Dataford
Interview QuestionsInterview GuidesExperiencesCoaching
New
Pricing
Get started
Dataford
Popular roles
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Browse
Browse All RolesEvery role hub, from analyst to MLBrowse All CompaniesCompany-specific interview loopsAll Interview GuidesThe full guide library
Top questions by role
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Top questions by skill
SQLPythonStatisticsMachine LearningA/B TestingSystem DesignGenerative AIProduct SenseMetricsBehavioral
Browse all questions →Try a mock interview
Experiences
Practice
Mock InterviewsTimed interview simulations with feedbackCoaching1:1 coaching with people at your target companiesSuccess PathYour 6-week structured planModulesCurated lessons by topicWebinarsTalks from ex-Big Tech data leadsPlaygroundA free-form scratch editor
Learn
BlogInterview strategy and career adviceTech Job Market ReportHiring trends across data and AI rolesFor UniversitiesDataford for career centersAbout DatafordWho we are and how we build
Pricing
Build my plan

Email Click Rate Significance Test

EasyStatistics & Probability00:00
Practice interviewer
In session
5 left
00:00

Your question is Email Click Rate Significance Test. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

You need to log in / sign up to chat or submit.

Problem

Business Context

BrightCart, an online retail platform, wants to know whether a new promotional email template improves click-through rate versus the current template. The marketing team ran a randomized A/B test on a single campaign.

Problem Statement

Use an appropriate statistical method to determine whether the new email template produced a statistically significant change in click-through rate.

Given Data

GroupEmails SentClicksClick-Through Rate
Control (current template)18,5001,5548.40%
Treatment (new template)17,9001,6479.20%

Additional test settings:

ParameterValue
Significance level0.05
Test typeTwo-tailed

Requirements

  1. State the null and alternative hypotheses.
  2. Identify the statistical method you would use and why it is appropriate.
  3. Calculate the pooled click-through rate.
  4. Compute the standard error and z-statistic.
  5. Calculate the two-tailed p-value.
  6. Construct a 95% confidence interval for the difference in click-through rates.
  7. Conclude whether BrightCart should treat the result as statistically significant.

Assumptions

  • Users were randomly assigned to control and treatment.
  • Each email outcome is independent.
  • Sample sizes are large enough for the normal approximation to hold.
  • The metric of interest is binary at the user level: clicked or did not click.