How to Write a Hypothesis: Building Testable Assumptions
An experiment's quality cannot exceed its hypothesis's quality. A test run on "let's see what happens" gets interpreted as "not bad, keep going" no matter the result because what counts as success was never written down. The hypothesis is the contract that stops you from deceiving yourself: written before the experiment, carrying a measurable threshold, and falsifiable.
The Formula
"We believe [segment] will show [behavior/problem/response]. We'll consider it validated if [experiment] produces [measurable result + threshold]."
Examples:
- "We believe boutique fashion e-commerce stores rank return costs among their top 3 problems. Validated if at least 10 of 15 store-owner interviews put returns in the top 3."
- "We believe busy professionals will pay for weekly meal planning. Validated if 300 targeted landing page visitors produce an 8%+ email signup rate."
- "We believe operations managers spend 4+ hours a week on shift planning. Validated if at least 8 of 12 conversations report over 4 hours."
The Hypothesis's Four Mandatory Properties
- One assumption: "Customers have this problem AND will pay for our solution" is two hypotheses; test them separately. A compound hypothesis hides which part collapsed
- A measurable threshold: Not "if we see interest" but "8% signups" / "10 of 15 interviews" / "5 pre-payments". A threshold-free hypothesis gets fitted to any result afterward
- Falsifiability: "Some users may find it useful" can't be falsified it's compatible with every outcome. A good hypothesis carries its collapse condition explicitly
- Experiment match: The hypothesis type determines the evidence tool
| Hypothesis Type | Evidence Tool | Example |
|---|---|---|
| Problem hypothesis | Customer interviews | "10 of 15 owners rank X in their top 3 problems" |
| Demand hypothesis | Landing page test | "8%+ email signup rate from 300 visitors" |
| Payment hypothesis | Pre-sales | "5 customers pay before the product exists" |
| Usage hypothesis | Prototype / MVP | "Weekly active use in the first 2 weeks" |
Where Do Thresholds Come From?
Setting a threshold feels arbitrary at first; use three anchors: industry references (rough bands like 5%+ signups on cold traffic being good, 20% B2B demo→close being healthy), business model requirements (the minimum conversion your unit economics need computed backward from your CAC target), and comparative baselines (the first experiment's result becomes the floor for the next). There is no perfect threshold; a written, reasoned threshold beats an unwritten perfect one. Defining a grey zone below the threshold is legitimate too: 8% target, 5-8% means "revise and retry," below 5% means "pivot signal."
Extracting Assumptions: Where Do Hypotheses Come From?
Hypotheses are mined from your business model: every box of the Business Model Canvas is scanned with "how do I know this is true?" Every item without evidence for an answer is an assumption; assumptions are scored on the criticality (does the model collapse if wrong?) × uncertainty (is there evidence?) matrix, and the top-right corner (critical + uncertain) enters the test queue. The typical early-stage order: segment reality → problem severity → solution acceptance → willingness to pay → channel → unit economics. Skipping steps (testing payment before validating the problem) renders results uninterpretable.
FAQ
My hypothesis was proven wrong is that failure?
The opposite it's the experiment working exactly as designed. A falsified hypothesis is the cheapest information that ever saved you months of building the wrong product. Progress here is validated learning, not code written. The critical step is next: why was it wrong, what new hypothesis emerges? Repeating the same test unchanged is data denial.
We nearly hit the threshold but missed (8% target, 7% result) now what?
Check sample size first small samples make a 7-vs-8% gap statistical noise, so extending the sample is legitimate (lowering the threshold after the fact isn't). Then read the qualitative layer: who signed up, which message won, what were the objections? One revision round is normal; three rounds still borderline means a real no.
How many hypotheses can I test at once?
Parallel experiments work under two conditions: they're independent one's result shouldn't change the other's design and each has separate, non-contaminating measurement. The practical early-stage limit is 2-3: one qualitative track (interviews) plus one quantitative track (landing page). Five simultaneous experiments usually leaves every sample too small for a clear decision.
Writing hypotheses feels bureaucratic is it really necessary in a small team?
The reverse it matters more in a small team, since you lack the runway to march months in the wrong direction. The format is one sentence, one threshold, one date a five-minute task. It buys decision clarity, team alignment, and a learning memory for six months later. The real bureaucracy is repeating an untested experiment three times.