Decision and prospective-plan gate
Locate a timestamped plan written before comparative outcomes were visible. It should name the decision owner, alternatives after positive, null, harmful, or invalid results, risky assumption, primary hypothesis, and smallest worthwhile effect. Check that later amendments disclose timing and outcome access. Center for Open Science guidance makes the planned-versus-exploratory distinction visible; use that distinction without pretending a plan can eliminate judgment. Fail the gate when the team cannot say what the result will change or when a newly favored metric appeared only after the original one disappointed.
Decision, owner, thresholds, and result-dependent actions were recorded in advance.
Confirmatory outcomes are separated from diagnostics and later exploration.
Amendments state their reason, timestamp, and whether comparative data were seen.
Evidence: Center for Open Science; UK Government Digital Service
Eligibility, randomization, and interference gate
Verify the eligible population, entry moment, assignment unit, planned ratio, stable allocation, and treatment versions. Check whether accounts, devices, sessions, regions, or time blocks match how effects and spillovers operate. Inspect observed allocation and pre-treatment invariants before outcomes. Investigate sample-ratio mismatch, cross-variant exposure, shared links, cached variants, and concurrent experiments. Do not exclude failed exposures if treatment caused them. The gate passes when assignment remains a credible source of comparison and the documented interference risk does not erase the contrast the analysis claims to estimate.
Observed assignment matches the configured ratio within the planned diagnostic rule.
Each unit remains consistently assigned and exposure failures are accounted for.
Spillover, concurrency, and contamination have bounded or measured consequences.
Evidence: National Institute of Standards and Technology; Encyclopedia of Machine Learning and Data Science
Instrumentation and data-lineage gate
Trace synthetic and real units from assignment through render, action, delayed outcome, aggregation, and dashboard. Compare server and client records, check duplicate and missing events, inspect release parity, and confirm consent and cross-domain behavior. Validate invariant metrics and perform an A/A or historical calibration when the operating context justifies it. Freeze metric formulas, units, windows, deduplication, and late-event handling. Fail the gate if a variant changes how an outcome is recorded, the raw lineage cannot be reproduced, or analysts silently repair only the treatment data after seeing the score.
Primary and guardrail events have reproducible definitions and independent spot checks.
Missingness, duplication, consent, delayed events, and release changes are quantified.
Quality failures stop interpretation rather than becoming a footnote under the winner.
Evidence: Encyclopedia of Machine Learning and Data Science; Center for Open Science
Power, duration, stopping, and multiplicity gate
Compare actual information with the planned baseline, decision-relevant effect, variability, power, and runtime. Confirm that relevant weekday, billing, novelty, or delayed-outcome cycles were covered. Determine whether the analysis is fixed-horizon or uses a prospectively valid sequential method. Inventory every confirmatory outcome, variant, contrast, and subgroup, then verify the specified multiple-comparison treatment. NIST explains why simultaneous comparisons need family-aware procedures. Fail the gate for opportunistic peeking, extending only an unfavorable test, ending on a favorable fluctuation, or presenting an underpowered null as proof that no meaningful effect exists.
The stop occurred under the declared information, time, quality, or safety rule.
All planned comparisons and any multiplicity adjustment are visible together.
Intervals are interpreted against decision-relevant effects, not only a threshold label.
Evidence: National Institute of Standards and Technology; Encyclopedia of Machine Learning and Data Science
Outcome meaning and harm-guardrail gate
Reconstruct the primary estimand in plain language: effect of offering which implementation, to which assigned population, on what unit and outcome, over which window. Confirm that it maps to the original decision rather than a convenient proxy. Review performance, errors, accessibility, support demand, cancellations, refunds, and affected groups before rollout. A click lift cannot compensate for material confusion or later harm. Require operational owners and rollback thresholds. Fail the gate when guardrails were chosen after the result, mature too late for the decision, or exclude the people most likely to experience the change's cost.
The primary outcome represents the intended benefit at the correct unit and horizon.
Guardrails cover operational quality, downstream consequences, and plausible harms.
Owners can stop, roll back, support, and investigate before damage spreads.
Evidence: UK Government Digital Service; Encyclopedia of Machine Learning and Data Science
Analysis reproducibility and interpretation gate
Run the planned code against a versioned extract and reconcile it with the displayed result. Verify analysis population, exclusions, aggregation unit, missing-data treatment, covariate adjustment, outlier handling, and interval calculation. Present the effect estimate, uncertainty, quality checks, and full guardrail table. Separate prespecified segment interactions from exploratory slices. A null result should state which beneficial and harmful effects remain compatible with the data. A positive result should not be converted into a universal behavioral explanation. Fail the gate when the conclusion depends on an undocumented filter or when only a p-value and percent lift survive review.
A reviewer can reproduce the main estimate from versioned code and data lineage.
Planned and exploratory results are labeled and the complete test family is retained.
Practical uncertainty and contradictory guardrails appear beside the headline result.
Evidence: Center for Open Science; National Institute of Standards and Technology; Encyclopedia of Machine Learning and Data Science
Attribution, decision, and expiry gate
Write the narrow causal statement the design supports, then list what it does not: mechanism for every person, effects outside eligibility, different implementations, longer horizons, other markets, or channel credit. Classify the run as valid and actionable, valid but inconclusive, exploratory, or invalid. Map the class to the prewritten action and record any override. Archive plan, versions, checks, analysis, decision, and unresolved questions. Set a later guardrail review and triggers for policy, offer, audience, or instrumentation changes. Passing the audit authorizes a bounded decision, never a permanent truth or an advertising claim larger than the tested evidence.
The attribution sentence names treatment, population, implementation, outcome, and window.
Decision and override follow a documented rule, with null and invalid states preserved.
Later effects and context changes have an owner, review date, and reconsideration trigger.
Evidence: National Institute of Standards and Technology; UK Government Digital Service; Center for Open Science
Sources and further reading
These references informed this article. A source supports a claim; it does not imply endorsement of TenMultigure or any future product reference.
- Completely randomized designsNational Institute of Standards and Technology · Accessed August 10, 2026
Grounds the assignment and bounded-attribution gates in a defined population of experimental units randomly allocated to treatments.
- Multiple comparisonsNational Institute of Standards and Technology · Accessed August 10, 2026
Supports auditing the full family of outcomes, variants, and pairwise claims rather than accepting one isolated favorable threshold.
- How the alpha phase worksUK Government Digital Service · Accessed August 10, 2026
Contributes the risky-assumption, proportionate-test, success-criteria, and decision evidence checks used at the beginning and end of the audit.
- Online Controlled Experiments and A/B TestsEncyclopedia of Machine Learning and Data Science · Accessed August 10, 2026
Provides field-tested online audit checks for power, sample-ratio mismatch, assignment, exposure, instrumentation, and misleading extreme results.
- Lifecycle Open ScienceCenter for Open Science · Accessed August 10, 2026
Supports timestamped plans, transparent deviations, reproducible records, and keeping null or exploratory outcomes connected to the original question.
Reviewed by TenMultigure Editorial Review. See an error or a source that has changed? Tell the editorial team.
Review method: AI-assisted desk research with editorial checks. Reviewed ; next scheduled review . Built seven evidence gates from prospective intent through allocation, lineage, multiplicity, harm, reproduction, and an explicitly expiring attribution claim.