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

Ship Decision for Call Routing Test

MediumA/B Testing & Experimentation00:00
Practice interviewer
In session
5 left
00:00

Your question is Ship Decision for Call Routing 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

Scenario

You work on an IT and communications product and have run an A/B test on a new inbound call-routing experience. The treatment appears to improve the main conversion metric, and stakeholders want to know if the result is statistically significant enough to ship rather than just a noisy win.

Question

How would you decide whether the result is statistically significant enough to ship? Walk through how you would define significance, practical impact, and guardrails before making the launch decision.

What This Tests

  • Hypothesis testing and statistical significance
  • Primary metric plus guardrail design
  • MDE-based power and sample-size reasoning
  • Awareness of peeking and experiment pitfalls