Apply Multiple Testing Corrections for Valid Results Analysis

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Apply Multiple Testing Corrections for Valid Results Analysis states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Apply Multiple Testing Corrections for Valid Results Analysis

Company: Attentive

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario A new message-sending feature is A/B-tested separately inside 30 companies (50/50 split, α = 0. 05). Results show 2 significant uplifts, 1 significant decline, and the rest are not significant. ##### Question What conclusions, if any, can you draw from these results? How should multiple testing be addressed, and which correction methods (e.g., Bonferroni, Benjamini–Hochberg) would you apply before declaring the feature effective? ##### Hints Compare expected false positives (30 × 0. 05) with observed, discuss family-wise vs. FDR control, and explain implications for rollout decisions.

Overview: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Apply Multiple Testing Corrections for Valid Results Analysis states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Attentive
Aug 4, 2025
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Apply Multiple Testing Corrections for Valid Results Analysis

Interpreting 30 Parallel A/B Tests With Mixed Results

Context

  • You ran 30 independent within-company A/B tests of a new message-sending feature (50/50 split).
  • Per-test significance level: α = 0.05 (assume two-sided unless pre-specified otherwise).
  • Outcomes: 2 significant uplifts, 1 significant decline, and 27 non-significant effects.

Tasks

  1. What conclusions can you draw from these results before and after accounting for multiple testing?
  2. How should multiple testing be addressed (family-wise error vs. false discovery rate), and which correction methods (e.g., Bonferroni, Holm, Benjamini–Hochberg) would you apply before declaring the feature effective?
  3. What are the implications for rollout decisions?

Hint: Compare expected false positives (30 × 0.05) with observed, discuss family-wise vs. FDR control, and explain implications for rollout decisions.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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