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name: trustworthy-experiment-insights description: Assess whether experiment results are credible enough to influence product decisions. Use when checking false positive or false negative risk, underpowered metrics, suspiciously large lifts, replication needs, meta-analysis, stratified sampling, covariate adjustment, or whether A/B test insights should be trusted. license: MIT compatibility: Codex, Claude Code, and other Agent Skills-compatible clients. metadata: version: "0.1.0" displayName: Trustworthy Experiment Insights category: Product Management tags: practical-ab-testing,next-level-ab-testing,ab-testing,experimentation,analytics

Trustworthy Experiment Insights

Use this skill to decide whether an experiment result is believable enough to shape a product or engineering decision. It focuses on false positives, false negatives, power, replication, meta-analysis, stratified sampling, covariate adjustment, and suspicious result review.

Source Traceability

Primary source: Next-Level A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from Chapter 6 on false positives and negatives, meta-analysis, metric sensitivity, stratified random sampling, covariate adjustments, replication, longer runs, and statistical power.

Related skills:

• ab-test-results-readout for standard experiment reporting.
• experiment-sensitivity-optimization for improving precision before or during experiment design.
• experiment-verification-monitoring for operational validity checks.

Reference Routing

Need	Read
Insight-quality concepts	references/core/knowledge.md
Credibility and follow-up rules	references/core/rules.md
Result-review scenarios	references/core/examples.md
Step-by-step credibility review	workflows/review-experiment-credibility.md

Workflow

1. Confirm the experiment was operationally valid enough to interpret.
2. Check power, practical significance, and whether metrics were underpowered.
3. Look for false positive risk: suspicious lift, many comparisons, early stop, weak prior, or contradiction with prior experiments.
4. Look for false negative risk: noisy metrics, small sample, low sensitivity, or over-broad metric choice.
5. Compare with similar experiments or run meta-analysis when available.
6. Recommend launch, replicate, extend, investigate, or reject the result.

Output Format

# Experiment Insight Credibility Review

## Result Under Review
[Experiment, metric, observed result, and proposed decision.]

## Credibility Assessment
[Trust | Trust with caveats | Replicate | Extend | Investigate | Do not trust]

## Evidence
| Check | Finding | Risk |
|-------|---------|------|

## Follow-Up
- Replication needed:
- Longer run needed:
- Meta-analysis/comparison:
- Variance reduction opportunity:

## Decision Guidance
[What decision can be made now, and what should wait.]

Quality Bar

• Do not celebrate a result before checking whether it could be a false positive.
• Do not dismiss a flat result before checking power and sensitivity.
• Do not compare against prior experiments without noting differences in population, metric, design, and timing.
• Do not use statistical checks to hide operational failures; verify experiment health first.