Experiment design: Validation and review

Provides evidence questions, review criteria, failure signals, and a practical validation checklist for experiment design. It fixes adoption boundaries and observable failure evidence while keeping environment-specific limits configurable.

Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.

Decision

For Experiment design, adopt a versioned product brief with named evidence owners. Treat the block as a release and regression contract built from representative positive, boundary, denial, failure, and recovery cases. This package is a reference contract: the adopting team must choose numeric limits, providers, jurisdictions, and operational owners from evidence in its own environment.

Scope

• The Experiment design actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes described by this testing block.
• Primary and alternate interfaces, background work, caches, integrations, support paths, and evidence that can exercise the same boundary.
• Adoption-specific configuration, rollout, recovery, and verification responsibilities needed to apply the reference safely.

Outside this block

• Choosing a universal vendor, framework, jurisdiction, numeric threshold, retention period, or service-level objective for every adopter.
• Claiming that packaged scenarios have run against a downstream implementation or that this reference grants legal, security, or accessibility certification.

Contract

• Distinguish observed evidence, explicit assumption, chosen constraint, and open question so a guess cannot silently become a requirement.
• Name target actors, situations, jobs, exclusions, current alternatives, expected outcome, and measurable signal.
• Keep MVP commitments, non-goals, later options, and irreversible decisions in separate lists with change rationale.
• Assign review dates and invalidation signals to material assumptions rather than treating discovery as permanently complete.
• Record fixture version, environment, configuration, observed result, and evidence location for every decision-bearing run.
• Separate product failure, dependency failure, test-infrastructure failure, and inconclusive evidence in reports.
• State the causal hypothesis, target population, assignment unit, intervention, comparator, primary outcome, guardrails, minimum detectable effect, and analysis plan before launch.
• Account for interference, novelty, seasonality, missing data, repeated exposure, sample-ratio mismatch, and stopping behavior and record deviations from the plan.

Implementation guidance

• Build the smallest deterministic fixture set that spans risk classes, then add production-derived cases only after privacy-safe review.
• Model Experiment design inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
• Store the applicable Experiment design contract or policy version with material state so migrations, replay, and support decisions remain attributable.
• Introduce the path behind controlled rollout, compare expected and observed outcomes, and keep a tested recovery route until adoption evidence is complete.

Failure handling and safeguards

• When evidence contradicts the brief, mark the affected decision unresolved and stop dependent scope from being treated as approved.
• Invalid or contradictory Experiment design state is rejected or quarantined; the implementation never guesses a value that broadens authority or duplicates an effect.
• Retries are bounded and use durable identity; after exhaustion, work reaches an inspectable terminal state with an accountable owner.

Verification and operations

• Run the suite twice from a clean state and confirm that ordering, retries, and parallel execution do not change the verdict.
• Monitor Experiment design success, denial, validation failure, dependency failure, retry exhaustion, and recovery by contract version without sensitive payload dimensions.
• Re-run paired positive and negative fixtures after policy, schema, dependency, migration, or boundary changes and preserve the resulting evidence.

Adoption assumptions

• Names and values in the example are a concrete fixture, not universal defaults; adopters replace them through documented evidence and ownership.
• The adopting system can provide authenticated identity, durable operation or record versions, bounded telemetry, and a controlled path for change.

The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.