How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a easy difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

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

Determine Significance of Model B's Performance Improvement

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 Determine Significance of Model B's Performance Improvement states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Determine Significance of Model B's Performance Improvement

A/B Test: Two-Proportion Z-Test for Success Rates

Scenario

You ran an A/B test comparing two large language models (LLMs):

Model A: 700 successes out of 1000 trials (p_A = 0.70)
Model B: 800 successes out of 1000 trials (p_B = 0.80)

Task

State the hypotheses to test whether Model B is better than Model A at α = 0.05.
Compute the two-proportion z-statistic (using the pooled standard error) and the corresponding p-value.
Decide if Model B is significantly better at α = 0.05.
Compute the 95% confidence interval for the lift (assume lift = p_B − p_A, the absolute difference in success rates).

Hint (pooled z-test):

z = (p_B − p_A) / sqrt(p*(1 − p)*(1/n_A + 1/n_B)), where p is the pooled proportion p = (x_A + x_B)/(n_A + n_B).

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

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?

Quick Overview

Task

State the hypotheses to test whether Model B is better than Model A at α = 0.05.

Compute the two-proportion z-statistic (using the pooled standard error) and the corresponding p-value.

Decide if Model B is significantly better at α = 0.05.

Compute the 95% confidence interval for the lift (assume lift = p_B − p_A, the absolute difference in success rates).

Hint (pooled z-test):

z = (p_B − p_A) / sqrt(p*(1 − p)*(1/n_A + 1/n_B)), where p is the pooled proportion p = (x_A + x_B)/(n_A + n_B).

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

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?

Determine Significance of Model B's Performance Improvement

Quick Overview

Determine Significance of Model B's Performance Improvement

Determine Significance of Model B's Performance Improvement

A/B Test: Two-Proportion Z-Test for Success Rates

Scenario

Task

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer

Determine Significance of Model B's Performance Improvement

Quick Overview

Determine Significance of Model B's Performance Improvement

Determine Significance of Model B's Performance Improvement

A/B Test: Two-Proportion Z-Test for Success Rates

Scenario

Task

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer