Template

Templates: Briefs, Hypotheses & Experiment Plans Pack (Interactive)

A ready-to-adopt pack containing an interactive one-page brief, structured hypothesis form, and repeatable experiment protocol form. Each form includes field-level guidance and required/optional flags so teams can submit, store, search, and link evidence for reliable, repeatable learning.

Interactive Tool

Templates: Briefs, Hypotheses & Experiment Plans Pack

This interactive pack implements the one-page brief, hypothesis statement, and experiment protocol as structured forms so teams can capture consistent, searchable submissions and preserve evidence. Copy and adapt these fields to your team domain, convert them to local forms, and connect submissions to dashboards or evidence stores.

Use this section to create a concise intake brief for scoping discovery, pilots or experiments. Keep it one page where practical.
Concise name for the initiative. Use a consistent prefix if desired (e.g., AREA - short name).
Who is responsible (owner) and which stakeholders need to be informed or approvers. Include names and roles or team identifiers.
One-sentence description of the problem or opportunity this brief addresses.
Short description of the idea or experiment type (A/B test, pilot, usability study, process change, etc.).
The single most important measure of success. Include numerator/denominator and exact definition (e.g., checkout completions / checkout starts).
What's included, excluded, time or budget bounds, target population, and any gating rules.
Top 2–3 risks and how the team will mitigate them.
What approval, resources, or next-step decision is needed now.
Expected start date, key checkpoints, and decision points.
Write hypotheses to be explicit about assumptions, expected change, and how you will know. Keep it testable and measurable.
Short context such as segment, product area, geography, or user cohort.
Use the format: If we [change], then [measurable outcome] will occur.
Exact metric name, numerator/denominator, and baseline value if known.
List key assumptions that must be true for the hypothesis to be valid (tracking accuracy, stable traffic mix, etc.).
Specific threshold, effect size, confidence level, or business rule for deciding success.
Who runs the test and how long it runs.
Use this protocol to turn a hypothesis into a reproducible experiment. Capture design, data, analysis, and final decisions.
Clear title and a unique ID or naming convention for tracking.
Short statement of the experiment objective and how it links to business outcomes.
Why this experiment matters and any relevant prior findings or research.
Link to the hypothesis statement or paste it here if not linked in system.
Define metrics, direction expected, and baselines for interpretation.
Define when to scale, iterate, or stop. Include statistical or business thresholds.
Describe treatment, control, variants, data sources, and sampling approach.
Target sample, power calculation notes, or gating rules. Attach calculations to evidence log.
Start date, intermediate checkpoints, and end date.
Owner, data analyst, implementer, approvers, and who will maintain the evidence log.
Where raw data, analysis code, and exported CSVs will live and how they'll be named.
What statistical tests, filters, and handling of missing or inconsistent data will be applied.
Document known biases and how the team will mitigate them.
Any user data or legal controls required (consent, retention, PII handling).
Attach or link to charts, code repo, raw exports, and the final analysis files.
Concluded action, whether to scale/iterate/stop, and three practical takeaways for future teams.
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