Experiment Audit for teams making repeated decisions from tests.
A structured review of a defined set of past, running, and planned experiments. I find recurring risks in audience selection, assignment, measurement, implementation, analysis, and rollout decisions.
Send the experiment set or concern as it is. I’ll reply with what is needed to scope the audit.
One questionable result may be a test problem. The same weakness across several tests is an experimentation-system problem.
Who this is for, and what changes after.
Teams that run experiments regularly but are unsure whether results generalize, past winners held after rollout, or the same setup and interpretation risks keep returning.
You get a reliability map across the reviewed experiments, confidence notes on past conclusions, recurring risk patterns, and a prioritized way to improve future tests.
The audit covers an agreed experiment set or time period. It is broader than reviewing one test, but it is not a general product strategy or full product-leak diagnosis.
The goal is not to relitigate every result.
The audit looks for patterns that change decision quality across tests: who enters, what exposure means, whether metrics represent value, whether implementation preserves the comparison, and how the team turns uncertainty into rollout decisions.
- What this prevents
- fixing symptoms instead of leaks
- metrics drifting away from user value
- experiments being interpreted without context
- roadmap work based on partial evidence
What gets audited.
Test Portfolio
Review the selected past, running, and planned experiments against the decisions they were meant to support.
Hypothesis Quality
Check whether tests begin with a causal product hypothesis or merely compare interface alternatives.
Audience Patterns
Look for recurring eligibility, exclusion, traffic-mix, and rollout-population mismatches.
Assignment & Exposure
Inspect repeated risks in randomization, assignment units, cross-device behavior, contamination, and exposure logging.
Metric System
Assess primary metrics, guardrails, metric definitions, and whether teams repeatedly optimize proxies instead of outcomes.
Implementation Reliability
Check whether tracking, feature flags, platforms, and releases preserve the intended experimental comparison.
Analysis & Interpretation
Review stopping rules, segment analysis, inconclusive results, novelty effects, and how uncertainty is communicated.
Decision Process
Trace how results become rollout, iteration, or roadmap decisions and where evidence is being overruled or overstated.
How the audit works.
Define the portfolio
We agree which past, running, and planned experiments matter and which decisions need confidence.
Review each evidence chain
I inspect hypotheses, audiences, assignment, exposure, metrics, implementation, analysis, and decisions.
Find recurring patterns
Individual findings are grouped into the systemic risks that repeatedly weaken results.
Improve the next cycle
You receive priorities, reusable review criteria, and a stronger shape for the next experiment.
What I deliver.
Experiment Reliability Map
A portfolio-level view of where the experimentation process is strong and where decision risk repeats.
Per-Test Confidence Notes
A concise assessment of which reviewed conclusions remain useful and which need caution.
Recurring Risk Patterns
The systemic audience, measurement, implementation, or interpretation issues that affect more than one test.
Prioritized Improvement Plan
The highest-leverage changes to test design, QA, analysis, and decision-making, ordered by impact.
Reusable Review Standard
A practical checklist your team can use before launch and before accepting a result.
Next Experiment Recommendation
A clearer shape for the next important test, grounded in the audit findings.
Need confidence across more than one test?
Send the experiment set, time period, or concern. I’ll reply with the information needed to scope the audit.