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LinkedIn·Product Manager·Onsite - Product Sense / Strategy·Senior

SeniorPrefer not to say
May 2026

Summary

LinkedIn PM interview, one question about experimentation on a past product. Pretty short on details but enough to know what they're looking for.

Questions Asked (1)

Q1

Walk me through an experiment you ran on a product you worked on previously.

A/B Testing & ExperimentationProduct Analytics & Metrics
Author's notes

I had a decent example ready but I fumbled the structure a bit.

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AI HintsAI Generated

Suggested Approach

Choose a specific experiment where you can clearly articulate the hypothesis, metrics, and outcome. Structure your answer using a framework like STAR, but emphasize the experimental design, data analysis, and decision-making process. Highlight how you collaborated with cross-functional teams and what you learned.

Pro tip: Quantify the impact of the experiment and discuss trade-offs, such as statistical significance vs. practical significance, to show depth. Also, mention how you ensured the experiment was ethical and unbiased.

1. Set the Context

Briefly describe the product, the problem you were solving, and why it was important. Mention the target user segment and business goal.

2. State the Hypothesis

Clearly articulate the hypothesis you were testing, including the expected outcome and the rationale based on data or user research.

3. Design the Experiment

Explain the experimental design: control and treatment groups, sample size, duration, and success metrics (primary and guardrail). Mention any tools used (e.g., A/B testing platform).

4. Analyze Results

Describe the results, including statistical significance and effect size. Discuss any unexpected findings or segments that behaved differently.

5. Decide and Iterate

Explain the decision made based on the results (ship, iterate, or kill) and the impact on the product. Mention next steps or follow-up experiments.

Key Points to Mention

  • Clear hypothesis and success metrics (e.g., click-through rate, engagement, revenue)
  • Experimental design details: randomization, sample size calculation, and control for confounding variables
  • Statistical analysis: p-value, confidence intervals, and practical significance
  • Cross-functional collaboration with engineering, design, and data science
  • Ethical considerations and guardrail metrics to avoid negative user impact
  • Learnings and how they informed future experiments or product strategy

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.