Apply Multiple Testing Corrections for Valid Results Analysis
Apply Multiple Testing Corrections for Valid Results Analysis
You ran 30 independent within-company A/B tests for a new message-sending feature, with a 50/50 traffic split for each test. The per-test significance level (alpha) was set at 0.05 (two-sided).
After running the tests, you observed the following outcomes:
- 2 significant uplifts
- 1 significant decline
- 27 non-significant effects
Your tasks are:
- What conclusions can you draw from these results before and after accounting for multiple testing?
- How should multiple testing be addressed (e.g., controlling the family-wise error rate vs. the false discovery rate)? Which correction methods (e.g., Bonferroni, Holm, Benjamini–Hochberg) would you apply before declaring the feature effective?
- What are the implications of your findings for rollout decisions?
Constraints & Assumptions:
- Preserve the scope, facts, inputs, and requested outputs from the prompt.
- If a detail is unspecified, state a reasonable assumption before relying on it.
- Keep the answer concise yet concrete enough to implement or evaluate.
AI interview prep
Core dilemma
Balancing the desire to detect true positive effects from multiple experiments against the increased risk of false positives due to repeated hypothesis testing.
2 tradeoffs and 2 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Apply Multiple Testing Corrections for Valid Results Analysis
Apply Multiple Testing Corrections for Valid Results Analysis
You ran 30 independent within-company A/B tests for a new message-sending feature, with a 50/50 traffic split for each test. The per-test significance level (alpha) was set at 0.05 (two-sided).
After running the tests, you observed the following outcomes:
- 2 significant uplifts
- 1 significant decline
- 27 non-significant effects
Your tasks are:
- What conclusions can you draw from these results before and after accounting for multiple testing?
- How should multiple testing be addressed (e.g., controlling the family-wise error rate vs. the false discovery rate)? Which correction methods (e.g., Bonferroni, Holm, Benjamini–Hochberg) would you apply before declaring the feature effective?
- What are the implications of your findings for rollout decisions?
Constraints & Assumptions:
- Preserve the scope, facts, inputs, and requested outputs from the prompt.
- If a detail is unspecified, state a reasonable assumption before relying on it.
- Keep the answer concise yet concrete enough to implement or evaluate.