Template

Experiment Record & Evidence Bank Template

A structured, savable experiment record that captures hypothesis, learning questions, plan, measurements, results, interpretation, evidence grading, and decision links so teams can discover and reuse experimental learning.

Interactive Tool

Experiment Record & Evidence Bank

Use this form to record an experiment so others can find, assess, and reuse what you learned. Capture hypothesis, plan, measurements, raw results, interpretation, and decisions. Save links to artifacts and grade the quality of evidence so organizational memory stays reliable.

A stable identifier you can use to reference this experiment (e.g., site-project-001). If left blank, one may be generated by the system.
A short descriptive title (what you tested).
Person or team responsible for the experiment and follow-up.
YYYY-MM-DD or local date format.
YYYY-MM-DD when experiment concluded (or expected end date).
Current lifecycle state of the experiment.
State the hypothesis you are testing (if X, then Y because...). Keep it specific and testable.
What specific question must this experiment answer to change a decision or practice?
Describe the intervention(s), control/comparison, sampling approach, assignment method, expected duration, and key operational steps.
Primary measure(s) used to judge success (include unit and target change).
Other metrics to observe for side effects, safety, or secondary insights.
Planned sample size, segments, or production scope (e.g., 2 stores, 100 users, 30 days).
Where data will come from, how it is collected, frequency, and who owns the data.
Statistical tests, comparison windows, aggregation rules, and thresholds for inference. Include how missing data will be handled.
Concise summary of the observed results. Attach or link to raw datasets where possible.
URLs, repository paths, dashboards, notebooks, or dataset identifiers where raw artifacts live. Use stable links when possible.
Provide main tables, charts, p-values, effect sizes, or model summaries that support your interpretation.
Explain what the results mean relative to the hypothesis and learning question. Note unexpected findings and plausible alternative explanations.
List known issues that limit how confidently the result generalizes or how clean the causal link is.
Tags help discoverability across the evidence bank.
A pragmatic quality label to help others triage records. See guidance below.
Rate the team's confidence that the conclusion is correct and actionable (1 = very low, 5 = very high).
1.0 10.0
If 'Yes', complete the Adoption Checklist and Decision Link fields. If 'No', list follow-up experiments or validation steps.
Concrete steps required to adopt this change at scale (roles, training, rollout plan, monitoring, rollback criteria).
Link the experiment to the formal decision, ticket, or playbook that records the resulting choice.
Who will do what next (e.g., replicate, scale, monitor), and expected timeline.
Record retention period, access restrictions, or any privacy/GDPR/PHI considerations for the artifacts or data.
Cross-reference earlier or concurrent experiments to help reviewers see the evidence chain.
Comma-separated terms to help discovery (e.g., churn, checkout, supplier-A).
Optional guidance for librarians, curators, or decision-makers reviewing this record.
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