📌 Interview Question
“You run an A/B test and get a p-value of 0.04. How would you interpret the result, and would you recommend launching the feature?”
This classic statistics question tests your ability to explain uncertainty, evaluate experiment results, and make pragmatic decisions based on data. It’s not just about stats—interviewers want to see that you understand the business implications of statistical findings.
✅ Step-by-Step Answer
1. Explain the Statistical Framework
Start by showing that you understand what a p-value measures in context.
“We’re testing whether the new feature has an effect on the key metric (e.g., click-through rate).
- H₀ (null hypothesis): There is no difference between control and treatment.
- H₁ (alternative hypothesis): There is a difference.”
A p-value of 0.04 means:
“If the null hypothesis were true, we’d expect to see a result this extreme or more extreme in only 4% of cases due to random chance.”
Since 0.04 < 0.05 (our typical α level), the result is statistically significant, and we can reject the null hypothesis.
2. Don’t Stop at Statistical Significance
Statistical significance tells you that the difference is unlikely due to chance, but it doesn’t tell you:
- How big the effect is
- Whether it matters for the business
“We should also look at the effect size and confidence intervals to judge whether the change is practically significant.”
3. Check Effect Size and Confidence Interval
Say the metric in question is click-through rate (CTR):
- Control group: 3.00%
- Treatment group: 3.12%
- Uplift: +0.12%
- 95% Confidence Interval: [0.02%, 0.22%]
This tells us:
- The true impact is likely between 0.02% and 0.22%.
- Even though statistically significant, the magnitude may be small.
“If the uplift is tiny, the engineering or UX cost of implementing the change might not be worth it.”
4. Consider Practical Trade-offs
Frame your thinking with business impact:
- What’s the cost of implementation?
- Does the change affect performance, UX, or scalability?
- Could there be negative side effects in other metrics?
- Is the 0.12% uplift meaningful at our scale?
“If we have 10 million daily impressions, a 0.12% CTR improvement means 12,000 more clicks per day, which could be significant for ad revenue.”
5. Discuss Risk and Recommendation
Now summarize your decision-making:
“I would recommend launching the feature if the uplift is:
- Statistically significant ✔
- Practically meaningful ✔
- Positive or neutral on secondary metrics ✔
- Technically feasible and low risk to implement ✔”
If any of the above conditions fail, recommend:
- Further testing
- A rollout to a subset of users
- Gathering more data
🧠 Bonus Insight: Common Misconceptions
| Misconception | Correct Interpretation |
|---|---|
| “P = 0.04 means there's a 4% chance the null is true.” | ❌ Wrong. The p-value tells you the chance of observing your result if the null were true, not the chance the null is true. |
| “Statistically significant means we should ship it.” | ❌ Not necessarily. You need business impact too. |
| “Larger sample size = more accurate effect size.” | ⚠️ Not always—especially if experiment design is flawed. |
🔍 Alternative Paths: Bayesian Inference
For advanced candidates, show awareness of Bayesian thinking:
“With a Bayesian approach, I could start with a prior belief about how much uplift to expect, then update it using the observed data to form a posterior distribution. That would help us reason probabilistically about the effect size.”
🧪 Summary Framework
| Step | What to Cover |
|---|---|
| 1 | Interpret p-value in context of hypothesis test |
| 2 | Highlight that significance ≠ importance |
| 3 | Evaluate effect size and confidence interval |
| 4 | Translate into business terms |
| 5 | Recommend based on a holistic view |
✅ Final Answer Summary (1-Min Version)
“A p-value of 0.04 means that if the null hypothesis were true, we’d see this result or more extreme in about 4% of cases. So the result is statistically significant at the 5% level.
But I’d also examine the effect size and confidence interval. If the uplift is small—say 0.1% CTR—we’d need to evaluate whether that justifies the engineering and UX costs. If the gain is meaningful and secondary metrics are stable, I’d recommend launching. Otherwise, I’d suggest gathering more data or doing a staged rollout.”
📘 Practice More
👉 Try questions like:
- “You run a test and p = 0.07. What next?”
- “What if your test is underpowered?”
- “How do you explain a confidence interval to a product manager?”