Template

Rapid Experiment Canvas

A one-page interactive canvas for short-cycle hypothesis tests. Capture hypothesis, metric & baseline, experimental design, decision rules, ownership, timeline, measurement approach, risks, and post-run outcomes so teams can run faster, safer experiments and preserve learning.

Interactive Tool

Rapid Experiment Canvas

Use this one-page canvas to design, run, and record short-cycle hypothesis tests. Fill in the planned fields before launch, and return to update results, decisions, and learnings after the run. Keep experiments small, measurable, and timeboxed.

State the assumption you want to test. Use: If [action], then [outcome], because [reason]. Example: If we show 'free shipping' on checkout, then conversion will increase by 3 percentage points because shipping cost is a common drop-off reason.
Name the single metric you'll use to judge success (e.g., conversion rate, clicks per visit, error rate).
Current value of the metric (use the same units, e.g., percent or count). Enter numeric value only.
Estimate the minimum change worth acting on (e.g., +3 percentage points or +10% relative). Helps set sample size and decision rules.
Describe the treatment, control (if any), sample selection, assignment method, and any segmentation. Be explicit about the variant and exposure logic.
Enter a planned sample size (number of users/events) or use this field for planned duration in days when sample size is unknown. If unsure, estimate a duration that is likely to capture enough events.
Where you'll measure the metric (tool or dashboard) and any key calculation details (time windows, filters). Note how to reproduce the numbers.
Define the precise rule that would lead you to adopt the change (e.g., 'metric increases >=3pp with p<0.05' or 'sustained improvement across 3 consecutive days'). Be concrete.
Define when you'll iterate rather than stop or adopt (e.g., 'directionally positive but below threshold; test a new variant for the same hypothesis').
Define when you'll stop the experiment (e.g., 'no meaningful lift after planned duration or negative impact on critical secondary metrics'). Include safety thresholds if applicable.
Person responsible for running the experiment and making the recommendation. Include role/name.
Plain text dates or duration. Keep it short-cycle (days or a few weeks).
Identify what could go wrong (customer impact, data quality issues) and how you'll reduce exposure (limit percentage of traffic, rollback plan, monitoring).
How strongly you expect the hypothesis to be true (1 low — 5 high).
1.0 10.0
Estimate how many days you plan to run the experiment.
Record the observed metric values, sample size achieved, statistical notes, and any anomalies encountered.
Outcome decision after evaluating results.
What you learned and suggested next experiments or actions. Capture both tactical and contextual insights.
Mark when the experiment is finished and the decision made.
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