MDIA1007 Chap.3 Risky Assumptions and Rapid Research
Risky Assumptions and Rapid Research
Week Three adds discipline to venture research by breaking a broad idea into beliefs and ranking them by consequence and uncertainty. The test-card method links one hypothesis to a method, metric and predeclared support and fail criteria. This chapter contrasts low-cost approval with behaviour that more directly bears on the venture.
It also shows how interviews, observation and simple experiments answer different questions, and why disconfirming evidence should lead to a recorded revision rather than be ignored or reinterpreted after the result.
What this chapter covers
- 01
Assumption inventory across customer, problem and behaviour
- 02
Priority through consequence and uncertainty
- 03
Hypotheses written as necessary venture beliefs
- 04
Method and metric alignment
- 05
Support and fail criteria fixed before results
- 06
Neutral reporting of disconfirming evidence
Worked example · free
Testing a costly action instead of general approval
- +1Write the belief as an action: residents with a suitable item will offer it for a specific nearby request within a stated period.
- +1Show a simple request board to a defined building group and record item-specific offers rather than opinions about sharing.
- +1Set support and fail criteria before launch, interview actors and non-actors, then report the context and limitations.
Key terms
- Assumption Inventory
- A list of beliefs that must hold across the audience, problem, channel, behaviour and solution.
- Critical Hypothesis
- The single consequential and uncertain belief selected for the current test.
- Behavioural Signal
- An observable action that bears more directly on a belief than verbal approval.
- Support Criterion
- A predeclared result that would increase confidence in the tested belief.
- Research Limitation
- A feature of the audience, method or context that narrows what the result can support.
Risky Assumptions and Rapid Research FAQ
How do I choose the riskiest assumption?
Select a belief that the venture depends on and for which reliable evidence is weak. A minor colour preference can be uncertain without being consequential. A central demand, trust or behaviour belief deserves priority when the venture would struggle if it were false.
Why set criteria before collecting data?
Precommitted criteria reduce the temptation to redefine success after seeing a disappointing result. They make the reasoning visible and let the team distinguish support, failure and an ambiguous middle. The criteria should be relevant to the stated belief rather than chosen simply because a metric is easy to collect.
Can interviews validate a behaviour assumption?
Interviews can explain experiences, motives and barriers, but stated intention is not the same as action. When the belief concerns use, payment or commitment, pair interviews with a relevant behavioural test. Report what each method showed instead of blending them into one stronger-sounding result.
What if the support criterion feels arbitrary?
Explain why the chosen rule would matter to the venture decision and whether the measure actually represents the belief. A classroom experiment will rarely establish a universal benchmark. Its criterion can still guide learning when it is declared in advance, proportionate to the sample and interpreted with an explicit limitation.
State what result would fail the belief, what result would be ambiguous and how a compromised method would be distinguished from a weak proposition before collecting any responses.
Assessment move
Take your current venture and write at least one assumption under customer, problem, behaviour, channel and solution. Place each assumption on a consequence-versus-uncertainty matrix, then defend the upper-right choice. Rewrite the selected belief as a sentence that could be contradicted. Design two methods and explain which one produces evidence closer to the action the venture needs.
Before running anything, write the measure, support criterion and fail condition. Practise with deliberately mixed outcomes so you can distinguish a failed hypothesis from a failed method. When reviewing course work, use a fixed order: belief, method, measure, criteria, outcome, analysis, limitation and next step.
This sequence mirrors the test cards and prevents later sections from becoming a general narrative detached from the actual experiment. Add a method stress test before collection: list one way recruitment, wording, technology and observer behaviour could distort the result. For each risk, state a control or limitation that would keep the claim honest.
After inventing a sample result, write three interpretations and rank them by how directly the evidence supports them. Reverse the outcome and check whether your criteria would still produce a clear decision. Compare interview intention with observable behaviour and say which one bears more directly on the assumption. If the method only looks convincing when results are favourable, redesign it before using participant time.
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