Product Discovery Canvas
Connects a critical hypothesis to test design, metric, threshold, guardrails, and a decision rule before testing begins.
An Experiment Canvas makes the team state what must be true, how it will test the claim, what it will measure, and which threshold triggers a decision. Activity is not evidence; the measure must fit the hypothesis.
Which critical assumption will we test through what observable behaviour, and which pre-set threshold decides?
Connect hypothesis, test, metric, threshold, and decision before launch; label post-hoc analysis as exploratory.
Hypothesis
Define one decision-relevant, falsifiable assumption.
Design
Choose the smallest ethical test that yields enough evidence for the decision.
Measure
Define a computable primary metric, data basis, and decision threshold.
Learn
Evaluate observed data against the pre-set rule and take the next decision.
Record evidence and decision after each run; create a new version for new assumptions rather than changing thresholds after results.
Useful for a clear learning decision with a controllable test. Use open-ended discovery research first when the question itself is unclear.
Discrete hypothesis, population, procedure, primary metric, threshold, minimum data, and guardrail.
Raw data, deviations, result against the pre-set rule, and justified next step.
Fictional worked example
Eight fields connect assumption, test, measurement, safeguards, and decision.
What must be true?
At least 30% of exposed active project leads start a CSV export.
Which investment depends on this?
Build an interactive prototype above threshold; otherwise revise the value proposition.
Who sees what, for how long?
Fake door for 200 eligible active accounts over 7 days.
Numerator, denominator, segment, window
Unique export starts / exposed active accounts in 7 days.
Which boundary applies before launch?
Success ≥30% with at least 150 exposed active accounts.
When to pause or stop?
Stop above 3% support complaints about the unavailable feature.
Which raw data and quality checks?
Exposure and click events, account segment, bot filter, deviation log.
What did the rule yield and what follows?
32% at n=181; guardrail 1.1%. Build prototype and test usability.
Start with the core question: Which critical assumption will we test through what observable behaviour, and which pre-set threshold decides?
Updates the solution tree with the test's learning and decision outcome.
Reorders the next risky assumption based on the test result.
Condenses the completed test evaluation into observation, insight, and next action while referencing the existing plan.
When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric.
It binds assumption, measurement signal, method, and evaluation into a compact structure and keeps the logic explicit. This clarifies before the test which observation counts as learning and which decision should be possible afterward.
The Experiment Canvas is filled out before every test and usually contains fields for the hypothesis, the riskiest assumption, the test design, the success metric, the threshold, and the learning goal. Common variants come from Ash Maurya and Strategyzer and are widely used in discovery coaching.
Start with the learning goal, not with the test design. Do not accept a hypothesis without a clear threshold.
The method guide walks execution through 4 phases, timing, 4 pitfalls, and clear stop criteria.
Open method guideYour perspective
Your feedback helps shape the next improvements to methodatlas.
Plan Experiment Canvas with real scope, suitable roles, and a clear decision objective.
To be confirmed
To be confirmed
Full learning cycle Time: 30–45 minutes to design; execution is separate
Complete this worksheet on paper or in your own document during the work.
What must be true?
...
Which investment depends on this?
...
Who sees what, for how long?
...
Numerator, denominator, segment, window
...
Which boundary applies before launch?
...
When to pause or stop?
...
Which raw data and quality checks?
...
What did the rule yield and what follows?
...
Strategyzer: Validate Your Ideas with the Test Card
Expected artifact: Testable hypothesis
Audience · behaviour · context · expected outcome
Entry:
...
Expected artifact: Test design
Population · intervention · duration · data capture · ethics
Entry:
...
Expected artifact: Measurement rule
Metric · formula · threshold · minimum data · guardrail
Entry:
...
Expected artifact: Learning card
Data · deviations · result · decision · new assumption
Entry:
...
...
Method guide: https://methodatlas.meierhoff-systems.de/en/methods/experiment-canvas/run-sheet
Fitting alternatives with decision guidance, followed by other related methods.
Instead of Experiment Canvas, choose Riskiest Assumption Test when you want to test the riskiest assumption first under real conditions.
Condenses hypothesis, test, metric, and success threshold onto one card before execution.
Moves customer problem, solution idea, and evidence toward a concrete result through "list assumptions", "map assumptions to dimensions", and "derive the test plan".
Separates discovery and delivery so that validated stories only move into implementation after learning loops.
Measures real interest in an idea that has not been built yet, through clicks, sign-ups, or other interest signals.
Build Measure Learn cycles turn risky assumptions into experiments that support persevere, pivot, or stop decisions.
