1. Start with a simple hypothesis template
Use a template that forces clarity: "If we [change], then [audience] will [behavior], because [reason]." This makes the expected behavior explicit and avoids vague outcomes like "improve engagement."
Example: "If we simplify the onboarding checklist, then new trial users will complete setup within 24 hours, because fewer steps reduce drop-off."
2. Name the audience precisely
Hypotheses fail when the audience is too broad. Instead of "users," specify the segment that matters: new trials, returning subscribers, mobile visitors, or a specific cohort.
Attach a segment definition you can track in analytics so the analysis matches the hypothesis.
3. Define the behavior change
State the behavior in observable terms: click, complete, submit, return. Avoid vague verbs like "engage" or "interact." If you cannot tie the behavior to an event, the hypothesis is not ready.
4. Connect to the primary metric
Every hypothesis needs a primary metric that will decide success. That metric should align with the behavior. If the hypothesis is about onboarding completion, the primary metric should not be revenue.
Choose one primary metric and list secondary metrics as guardrails.
5. Add the reasoning
Great hypotheses include a reason based on evidence. That evidence could be user research, support tickets, or past experiment learnings. Write one sentence explaining why you expect the change to work.
Turn hypotheses into a habit
Make hypothesis writing a standard step in every experiment brief. Over time, the quality of your tests improves because the team has practiced being specific.