Choose evidence for a product assumption
Is stated interest enough to justify building the whole product?
Entering midway? Revisit Observe the task before proposing a feature for the preceding worked example, or start here and use the explanation below.
Start with a concrete question
Separate fictional cohorts answer a survey, attempt a prototype or join a reversible trial at a stated price. No transaction occurs.
Try the initial inputs, then write a prediction before changing one setting. All calculations run locally on your CPU.
What do you expect?
Before changing a setting: Is stated interest enough to justify building the whole product? Write what you expect and why.
Change one thing. Inspect what happens.
- Compare evidence types at the same price.
- Change price while retaining the evidence type.
- State which assumption each action addresses and which it leaves unresolved.
- Design a small reversible test with a decision rule and stop condition.
Your settingsMechanism
Calculation belowEvidence
Compare outcomes
Follow the calculation
- Price shown: 0 fictional credits.
- Evidence collected: survey says would use.
- 80 of the same-sized 100-person fictional cohorts take that action. Cohorts are different; these are not a measured conversion funnel.
- No actual payment is requested or processed. A reversible commitment is still not evidence of long-term retention.
Choose an experiment that tests the riskiest assumption. Interest, task success and commitment answer different product questions.
Captures keep your inputs, prediction and observation. Results are recalculated from those inputs. Resetting inputs keeps your captures and challenge history.
Explain the mechanism
Choose evidence for a product assumption
Separate fictional cohorts answer a survey, attempt a prototype or join a reversible trial at a stated price. No transaction occurs.
Follow the mechanism
Interest, successful use and commitment are distinct observable actions.
Because cohorts differ, these counts cannot be multiplied as a conversion funnel.
A reversible trial may reduce uncertainty but does not establish retention, profitability or broad demand.
Worked example
Price shown: 0 fictional credits.
Evidence collected: survey says would use.
80 of the same-sized 100-person fictional cohorts take that action. Cohorts are different; these are not a measured conversion funnel.
No actual payment is requested or processed. A reversible commitment is still not evidence of long-term retention.
Calculation
Observed action proportion = positive actions / 100 in each separate fictional cohort.
Try a controlled comparison
Compare evidence types at the same price.
Change price while retaining the evidence type.
State which assumption each action addresses and which it leaves unresolved.
Design a small reversible test with a decision rule and stop condition.
What this does not establish
- All messages, people, documents and outcomes here are original fictional teaching fixtures; results are not measurements of real organizations.
Try a fresh case
In a new trial, 18 of 60 invited people make a reversible commitment. What percentage commit?
This is a public practice check. Retries support learning; matching a number does not certify independent proficiency.
Take the investigation further
Choose any extension. These are project prompts for you to carry out; they are not automatically assessed.
School investigation
Create a new case with different numbers and explain your decision to a classmate.
College investigation
Implement or analyze a fresh case, document assumptions and compare at least two alternatives.
Workplace investigation
Prepare a decision artifact with evidence, operating constraints, an owner and a follow-up measurement.
Keep the evidence, choose your next step
Completion records your own decision that you have worked through this investigation. Practice checks and independent project evidence are separate.