Assumption Mapping
Sort your assumptions by importance and evidence so you know exactly which one to test first.
10 min · Popularised alongside Lean Startup and customer development practice
What is this framework?
Every business idea rests on a pile of unproven assumptions. Assumption Mapping plots each one on two scales — how important it is to your success, and how much evidence you already have for it — so you can clearly see which assumptions to test immediately, which to monitor, and which to leave alone for now.
Founders often have dozens of untested beliefs buried inside their plan: that customers have the problem, that they will pay, that a channel will reach them, that a partner will cooperate, that a regulation permits the model. Not all of these carry equal risk, and treating them as equally urgent wastes limited time and money.
Assumption Mapping uses a simple two-axis grid: importance on one axis (how much the venture depends on this being true) and evidence on the other (how much proof you already have). The assumptions with high importance and low evidence are your biggest blind spots — they sit in the danger zone and must be tested first, before anything else.
This tool is deliberately quick and visual. It is not meant to replace testing itself, but to make the prioritisation decision explicit and shared across a team, so that everyone agrees on what genuinely needs to be proven before the venture invests further resources.
What problem does it help solve?
- Prevents wasting effort testing low-stakes assumptions while ignoring dangerous ones
- Creates a shared, visual way for a team to agree on priorities
- Surfaces hidden assumptions that were never stated out loud
- Feeds directly into designing the next experiment
- Reduces the chance of being blindsided by an assumption nobody checked
The framework
Importance: low → high
Test first (high importance, low evidence)
Your biggest blind spots — test these immediately, before anything else.
Keep validating (high importance, high evidence)
Already supported by evidence, but critical enough to keep monitoring.
Low priority (low importance, low evidence)
Untested but not dangerous — revisit later if the venture evolves.
Assume and move on (low importance, high evidence)
Well supported and not critical — no need to spend more time here.
Evidence: low → high
The top-left quadrant (high importance, low evidence) is where your next experiment should come from.
Every part explained
Assumption
A specific, falsifiable statement your venture depends on, written as a claim rather than a vague hope.
Ask: What exactly am I assuming to be true, in one clear sentence?
Example: Students will walk an extra 200 metres to use a cheaper printing kiosk.
Importance
How much the venture's survival depends on this specific assumption being true.
Ask: If this assumption turns out false, does the whole idea fail?
Example: If students won't walk the extra distance, the kiosk location choice fails entirely.
Evidence
How much real proof — not opinion — you currently have that the assumption is true.
Ask: What proof do I actually have for this today, beyond my own belief?
Example: None yet — this is based on the founder's personal guess.
Quadrant placement
Plotting the assumption on the importance/evidence grid to reveal its priority.
Ask: Given importance and evidence, which quadrant does this fall into?
Example: High importance, low evidence — placed in the 'test first' quadrant.
Test design
Once priority is clear, designing the specific experiment to gather evidence for top-quadrant assumptions.
Ask: What is the fastest way to get real evidence on this specific assumption?
Example: Place a temporary sign 200 metres from the current printing point and count foot traffic.
Worked example — A campus parking coordination app (student brief scenario)
A team plans an app to help students share information about available parking spaces near lecture halls.
Assumption A
Students experience real difficulty finding parking daily (importance: high, evidence: some — anecdotal complaints).
Assumption B
Students will open a separate app specifically to check parking before driving (importance: high, evidence: none).
Assumption C
Students will accurately report space availability when they leave (importance: high, evidence: none).
Assumption D
The university will allow signage or sensors on campus grounds (importance: medium, evidence: none).
Assumption E
Students already use a general campus app that could add this feature (importance: low, evidence: high — confirmed via existing app usage data).
Mapping result
B and C fall into 'test first': high importance, zero evidence, and the entire business model depends on both being true simultaneously.
Next step
Design a manual experiment: ask 30 students leaving a car park to report their space verbally to a volunteer, and measure how many actually do it.
The map showed that the app's core behavioural assumption — that students will report and check parking status — was the real risk, not the technology.
How to use it
- 1List every assumption underlying your venture, written as clear, testable statements.
- 2For each one, rate its importance to the venture on a low-to-high scale.
- 3For each one, rate how much real evidence you currently have on a low-to-high scale.
- 4Plot every assumption onto the two-axis grid based on these two ratings.
- 5Identify the assumptions sitting in the high-importance, low-evidence quadrant.
- 6Choose the single most urgent one from that quadrant to test next.
- 7Design and run an experiment specifically for that assumption.
- 8Update the map with new evidence and repeat.
Try it yourself
List the assumptions behind your idea and place each one in a quadrant.
Assumption 1
Assumption 2
Assumption 3
Your work stays on this device. Nothing is uploaded, so use the same browser to come back to it.
When to use it
- Right after you have a rough business idea but before building anything
- Whenever a team disagrees about what to test next
- Before writing an experiment canvas, to choose which assumption deserves one
- Periodically throughout a venture's life as new assumptions appear
When not to rely on it
This framework does not prove:
- • The importance and evidence ratings are subjective unless the team discusses and agrees on them
- • It only prioritises assumptions; it does not test them for you
- • Assumptions can be mis-stated too broadly or too narrowly, distorting the mapping
- • A map done once can go stale quickly as the venture and market change
Common mistakes
- Writing vague assumptions that cannot actually be tested or proven false
- Rating everything as 'high importance' so the map fails to prioritise anything
- Confusing confidence or enthusiasm with actual evidence
- Mapping assumptions once and never updating the grid as new evidence arrives
Connections
Related concepts
Related frameworks
Quick check
Which assumptions should be tested first?
Remember this
Test the assumptions that matter most and that you know the least about.