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

Estimate Lift and Significance in Facebook Ad Campaigns

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competence in estimating conversion lift and testing statistical significance for randomized online advertising experiments, focusing on inference for binary outcomes, sample-size/power calculations, and Bayesian reframing.

Measuring Conversion Lift from Facebook Ads

Scenario

An advertiser is running a randomized experiment on Facebook. Users are split into:

Control (unexposed to ads)
Test (exposed to ads)

For each group you have:

n_c, x_c = number of users in control, number of conversions in control
n_t, x_t = number of users in test, number of conversions in test

Assume conversion is binary per user (converted at least once). If only impression-level exposures are available, interpret n as unique users reached, not total impressions.

Question

How do you estimate the conversion lift and test its statistical significance?
How large a sample is required to detect a 5% relative lift with 90% power (state assumptions)?
If a London stakeholder requests a Bayesian approach, how would you re-frame the analysis?

Hints

Two-proportion z-test or Bayesian posterior for lift; power calculation formula.

Solution

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