Hypothesis Formation
Hypothesis formation is the discipline of turning a diagnosed funnel leak into a specific, falsifiable statement that names the change, the mechanism, the primary metric, and the expected lift. A well-formed hypothesis is the contract between the diagnosis and the experiment. It is what makes a result readable.
- A hypothesis is falsifiable. If it cannot be wrong, it cannot be tested.
- Every hypothesis names one primary metric. Secondary metrics are guardrails, not tie-breakers.
- Expected lift is anchored in the diagnosis, not in optimism.
- The hypothesis is written before the experiment is scoped, not after the data comes back.
Where does this stage earn its keep?
Teams skip straight from a diagnosis to a design change. When the result comes in, no one agrees on what the test was actually measuring, and the winner gets rolled back three months later because a downstream metric quietly regressed.
Terms this stage depends on.
A statement structured so that a defined outcome would disprove it. The opposite of a directional intent.
The single metric the experiment is powered to move, chosen because it maps to the sized opportunity.
A metric the experiment is required not to harm, monitored throughout the test.
What changes when this stage is done properly.
Illustrative comparison of hypothesis quality.
| Metric | Gut-driven attribution | Fully-instrumented data pipeline |
|---|---|---|
| Specificity | "Redesign the pricing page." | "Adding annual toggle raises trial-to-paid by 20% relative because it anchors on the lower monthly-equivalent price." |
| Metric | Not named. | Named. Powered. Pre-registered. |
| Guardrails | Discovered post-hoc. | Chosen up front. Monitored during the test. |
The operational shape of this stage.
- 01Translate every sized opportunity into a hypothesis in the form: change, mechanism, primary metric, expected lift, guardrails.
- 02Pre-register the hypothesis in the experiment log before scoping the design.
- 03Reject hypotheses that cannot be resolved within the traffic available in a reasonable cycle.
Hypothesis from a diagnosed SaaS trial funnel
A PLG SaaS diagnosis surfaced that free-trial users who invited a second seat converted to paid at materially higher rates. The hypothesis: prompting a seat invite inside the onboarding checklist will raise trial-to-paid by roughly 30% relative, powered on trial-to-paid as the primary metric, with activation-per-user and support-ticket volume as guardrails. The test ran and read out at a 33% relative lift at p=0.01.
Frequently asked about this stage.
What makes a growth hypothesis falsifiable?
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What makes a growth hypothesis falsifiable?
+How many hypotheses should be in flight at once?
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How many hypotheses should be in flight at once?
+See this stage run against your numbers.
A 30-minute Growth Audit. You leave with two or three specific findings, whether or not we ever work together.
Field notes on measurement, experimentation, and growth for health and SaaS. No fluff.
Occasional dispatches from real engagements — attribution, revenue engineering, digital-health growth. Sent when there is something worth reading, not on a drip schedule.