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Statistics & Experimentation Interview Questions

Review this list of 79 Statistics & Experimentation interview questions and answers verified by hiring managers and candidates.

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What are the common pitfalls of A/B testing?
Statistics & Experimentation
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Statistics & Experimentation
How would you measure engagement for a productivity app? What features or behaviors would you track?
Product Analyst
Statistics & Experimentation
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Product Analyst
Statistics & Experimentation
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The product team reports a decline in 30-day user retention. How would you approach this problem?
Data Analyst
Statistics & Experimentation
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Data Analyst
Statistics & Experimentation
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How do you handle network effects in A/B testing?
Statistics & Experimentation
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Statistics & Experimentation
If you sample 10,000 users multiple times, what would the distribution of false positives look like?
Statistics & Experimentation
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Statistics & Experimentation

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What are the Z and t-tests?
Statistics & Experimentation
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Statistics & Experimentation
The leadership team wants to understand the ROI of a new feature—how would you structure that analysis?
Data Analyst
Statistics & Experimentation
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Data Analyst
Statistics & Experimentation
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You’re given product adoption data across regions—how would you determine where the rollout was most successful and why?
Data Analyst
Statistics & Experimentation
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Data Analyst
Statistics & Experimentation
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How do you detect novelty or primary effects in experiments?
Statistics & Experimentation
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Statistics & Experimentation
How would you design an analysis to understand what drives long-term user retention in a subscription service?
Statistics & Experimentation
Data Analysis
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Statistics & Experimentation
Data Analysis
What if your test metric is not normally distributed?
Statistics & Experimentation
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Statistics & Experimentation
Why might a statistically significant result not always be practically significant?
Statistics & Experimentation
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Statistics & Experimentation
How can you verify that assignment to A/B testing groups was truly random?
Statistics & Experimentation
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Statistics & Experimentation
Why might an unbiased estimator not always be preferred over a biased one?
Statistics & Experimentation
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Statistics & Experimentation
How would you measure correlation in the presence of outliers?
Statistics & Experimentation
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Statistics & Experimentation
Describe hypothesis testing and p-values in layman’s terms.
Statistics & Experimentation
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Statistics & Experimentation
Explain the statistical background behind power.
Statistics & Experimentation
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Statistics & Experimentation
How would you explain the importance of sampling distribution in inferential statistics?
Statistics & Experimentation
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Statistics & Experimentation
Compare covariance and correlation.
Statistics & Experimentation
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Statistics & Experimentation

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Statistics & Experimentation Interview Questions

What are the common pitfalls of A/B testing?

How would you measure engagement for a productivity app? What features or behaviors would you track?

The product team reports a decline in 30-day user retention. How would you approach this problem?

How do you handle network effects in A/B testing?

If you sample 10,000 users multiple times, what would the distribution of false positives look like?

What are the Z and t-tests?

The leadership team wants to understand the ROI of a new feature—how would you structure that analysis?

You’re given product adoption data across regions—how would you determine where the rollout was most successful and why?

How do you detect novelty or primary effects in experiments?

How would you design an analysis to understand what drives long-term user retention in a subscription service?

What if your test metric is not normally distributed?

Why might a statistically significant result not always be practically significant?

How can you verify that assignment to A/B testing groups was truly random?

Why might an unbiased estimator not always be preferred over a biased one?

How would you measure correlation in the presence of outliers?

Describe hypothesis testing and p-values in layman’s terms.

Explain the statistical background behind power.

How would you explain the importance of sampling distribution in inferential statistics?

Compare covariance and correlation.

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