Explain difference-in-differences with an example

Last updated: January 18, 2026

Quick Overview

Explain difference-in-differences in simple terms and provide a concrete example.

OpenAI
Statistics & Math
Data Scientist
OpenAI
January 18, 2026
Data Scientist
Phone Screen
Statistics & Math
Hard

523

5

2,070 solved


Explain difference-in-differences in simple terms and provide a concrete example.

This statistics question from OpenAI's Phone Screen tests your ability to apply mathematical reasoning to practical problems. The interviewer expects precise definitions, correct methodology, and awareness of assumptions and limitations.

What the Interviewer Expects
  • Derive results from first principles when needed
  • Handle complex scenarios with multiple interacting variables
  • Design experiments that account for real-world complications
  • Discuss advanced topics: Bayesian methods, causal inference, resampling
  • Connect statistical concepts to business decision-making
  • Identify subtle errors in reasoning (Simpson's paradox, survivorship bias)
Key Topics to Cover
Multiple testing correction (Bonferroni, FDR)
Power analysis and sample size calculation
Confidence intervals and significance levels
Bayesian vs frequentist inference
Hypothesis testing (H0, H1, p-values)
Causal inference basics
How to Approach This
  1. Define your hypotheses (H0 and H1) clearly before performing any test.
  2. Calculate required sample size BEFORE running an experiment, using power analysis.
  3. Remember the Central Limit Theorem: sample means become approximately normal with large n.
  4. Watch for Simpson's paradox. Always segment data by key dimensions.
  5. Distinguish between statistical significance and practical significance.
Possible Follow-up Questions
  • How would you design a follow-up experiment based on these results?
  • What if the sample size is very small?
  • What alternative statistical method could you use here?
  • What assumptions does this test make, and how would you validate them?
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Problem Formulation

Difference-in-differences (DiD) is a statistical technique used to determine the causal effect of a treatment or intervention by comparing the changes in outcomes over time between a treatment group a...

Solution Approach

To derive the DiD estimator, follow these steps:

  1. Measure the outcomes for both groups before and after the intervention.
  2. Calculate the change in outcomes for the treatment group and the control ...

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