Independent Measurement & Statistical Verification for App A/B Tests
Flow Harbor Point provides rigorous statistical audits, telemetry diagnostics, and variance reduction analysis for product engineering teams. We verify whether observed metric movements represent true behavioral lifts or instrumentation artifacts before production deployment.
Mobile Checkout Redesign
Comprehensive Experiment Result Audit & Statistical Verification
A deep-dive, independent statistical evaluation of completed or in-flight A/B tests to establish empirical validity before committing irreversible engineering resources.
Engagement Scope & Deliverables
When product experiments yield surprising gains, borderline p-values, or conflicting metric movements, our consultants conduct end-to-end data validation. We reconstruct your sample distributions directly from raw event logs, test for hidden sample ratio mismatches, filter extreme out-of-bounds variance, and apply covariate-adjusted variance reduction.
- ▸ Sample Ratio Mismatch (SRM) Inspection: Chi-square test across client versions, regions, and device tiers.
- ▸ Variance Adjustment (CUPED): Utilizing pre-experiment covariates to narrow confidence intervals by up to 40%.
- ▸ Dual Statistical Verification: Cross-evaluating frequentist confidence bounds with Bayesian posterior distributions.
- ▸ Formal Executive Briefing: Signed statistical report with concrete release/abandon recommendations.
Who This Consultation Is For
This engagement is tailored for engineering leads, product directors, and analytics managers facing high-stakes product decisions:
Critical Funnel & Revenue Alterations
Where a 1% false positive or negative translates into tens of thousands in annual subscription or checkout variance.
Telemetry Discrepancies
When backend database receipts conflict with in-app tracking logs, creating conflicting variant performance conclusions.
Novel Architecture Rollouts
Evaluating complex redesigns where secondary guardrail metrics (crash rates, battery drain, network latency) must not degrade.
End-to-End Experimentation Practice Areas
Comprehensive statistical support at each phase of your testing lifecycle, from initial sample power calculation to complex guardrail metric synthesis.
Pre-Experiment Power Sizing & Variance Modeling
Eliminate underpowered tests before writing code. We calculate required sample sizes, minimum detectable effect (MDE) boundaries, and historical metric variance baselines to establish required runtime.
View Practice Details →Sample Ratio Mismatch (SRM) & Telemetry Diagnostics
Detect sample imbalances, assignment leakage, drop-off bias, and network payload drops that silently corrupt test variants and produce artificial statistical significance.
View Practice Details →Guardrail & Multi-Metric Evaluation
Safeguard business health by establishing statistical boundaries across primary revenue drivers, latency metrics, uninstalls, customer support escalations, and 30-day retention.
View Practice Details →The 4-Fold Experiment Measurement Protocol
Our proprietary verification methodology unfolds in four sequential analytical facets to ensure mathematical integrity.
Telemetry Ingestion & Integrity Check
We verify event timestamp synchronization, client-side caching delays, deduplication rules, and schema consistency across iOS, Android, and web clients.
SRM & Assignment Stratification
Chi-square goodness-of-fit testing against intended allocation splits, stratified across device OS, network latency buckets, and new vs returning user cohorts.
Variance Reduction (CUPED) & Inference
Application of Controlled-experiment Using Pre-Experiment Data (CUPED) to remove background variance, followed by dual Frequentist p-value and Bayesian credible interval modeling.
Decision Boundary & Rollout Memo
Translation of raw statistical outputs into a clear, risk-weighted executive recommendation detailing rollout thresholds, holdout group sizing, and guardrail limits.
Selected Case Evidence & Reviews
Read how independent statistical audits resolved critical testing ambiguities for engineering and growth teams.
"During our mobile subscription redesign test, our internal dashboard reported a +6.1% conversion uplift. Flow Harbor Point's audit discovered a subtle SRM caused by older Android client caching. Once adjusted, true lift was +1.9%, saving us from overestimating quarterly recurring revenue projections."
"We commissioned a pre-experiment variance sizing for our search re-ranking test. Their team calculated that our planned 1-week test had only 42% power to detect our 1.5% MDE. Extending the run to 18 days with CUPED gave us definitive statistical clarity without guessing."
"The written audit brief was exceptionally thorough. The only minor friction was the extensive initial data formatting required on our end to match their intake schema, but the resulting Bayesian confidence distribution and rollout risk assessment were indispensable."
Have a Critical Experiment Awaiting Rollout Decision?
Contact our Hat Yai practice office to discuss your test dataset, telemetry architecture, and statistical verification requirements. We provide initial scope assessments within one business day.