Act when A/B result is not significant

Quick Overview

This question evaluates a candidate's competency in experimental design and statistical decision-making, covering sample size calculation, variance-reduction methods, interpretation of non-significant A/B results, multiplicity control, and sequential/Bayesian decision frameworks.

Act when A/B result is not significant

Company: TikTok

Role: Data Scientist

Category: Statistics & Math

Difficulty: hard

Interview Round: Onsite

An A/B test for the 60s video change yields a non-significant lift on the primary metric at α = 0.05. Baseline completion rate is 22%. Product’s minimum meaningful effect is +5% relative (to 23.1%). At 80% power, two-sided test, equal allocation: (1) compute the required per-arm sample size and explain your formula/assumptions; (2) propose variance reduction (e.g., CUPED with pre-period completion, covariate adjustment) and estimate the power gain if R² = 0.25; (3) decide whether to continue collecting data, stop, or pivot to a Bayesian decision with a utility-based threshold; (4) discuss multiplicity control if multiple related outcomes are monitored; and (5) outline a sequential design (e.g., O’Brien–Fleming) that would have preserved Type I error while allowing early looks.

Quick Answer: This question evaluates a candidate's competency in experimental design and statistical decision-making, covering sample size calculation, variance-reduction methods, interpretation of non-significant A/B results, multiplicity control, and sequential/Bayesian decision frameworks.

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Oct 13, 2025, 9:49 PM
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A/B Test Planning and Decision-Making for a 60s Video Change

Context: You are evaluating a product change with completion rate as the primary metric. Baseline completion is 22%. The product team defines the minimum meaningful effect (MME) as a +5% relative lift, i.e., from 22.0% to 23.1% (an absolute +1.1 percentage points). Use a two-sided test with α = 0.05, 80% power, and equal allocation.

Tasks

  1. Sample size: Compute the required per-arm sample size to detect the MME and explain the formula and assumptions used.
  2. Variance reduction: Propose variance-reduction methods (e.g., CUPED with pre-period completion, covariate adjustment) and estimate the power gain if R² = 0.25.
  3. Decision: Given that the current test shows a non-significant lift at α = 0.05, decide whether to continue collecting data, stop, or pivot to a Bayesian, utility-based decision rule.
  4. Multiplicity: Discuss how you would control multiplicity if multiple related outcomes are monitored.
  5. Sequential design: Outline a sequential design (e.g., O’Brien–Fleming) that preserves Type I error while allowing early looks.
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