Hypotheses and assumptions are testable statements about your users, market, and product. They help you document what you think you know, prioritize what to validate first, and track validation progress systematically.
What is a Hypothesis?
A hypothesis is a testable statement about your problem or solution that can be validated or invalidated through research, experiments, and user feedback. Assumptions are beliefs you hold that may not yet be tested.
Why Hypotheses Matter
By making your assumptions explicit, you can prioritize which beliefs to test first, track validation progress, and make data-driven decisions based on evidence rather than opinions.
Creating Hypotheses
To create new hypotheses, visit the Hypotheses page where you can:
Hypotheses Section
Write from Scratch: Start with a blank hypothesis and write your assumptions manually
Generate with AI: Use Spells to automatically generate hypotheses based on personas, markets, or other context
Import from Research: Attach hypotheses to existing personas, validation data, trials or insights
Using the Hypothesis Management Interface
The hypothesis interface provides multiple views and tools for managing your assumptions effectively.
Grid View
The default view showing all hypotheses as individual cards:
Hypothesis Cards: Each hypothesis displayed with state, description, and priority score
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Impact: /10
Confidence: /10
Effort: /10
State Management: Change validation status with dropdown selectors
Priority Scoring: Automatic scoring based on impact, confidence, and effort
Reference Linking: Connect hypotheses to personas, markets, insights, and other objects
Support Grading: Model how impacts support or contradict a hypothesis and compute an overall support score
Sorting Options: Sort by updated date, creation date, score, state, or dimension
Hypothesis States
Track validation progress
Unvalidated: New assumptions not yet tested
Validating: Currently being tested or researched
Validated: Confirmed through evidence
Invalidated: Disproven through evidence
Ignored: Decided not to pursue
Obsolete: No longer relevant
Board View
Kanban-style board for tracking hypothesis validation progress:
Hypotheses Board
Status Columns: Unvalidated, In Progress, and Completed
Drag & Drop: Move hypotheses between states by dragging cards
Board Management
Use the hypothesis selector to add existing hypotheses to your board, or create new ones directly in the unvalidated column.
Two-by-Two Prioritization
Interactive matrix for prioritizing which hypotheses to validate first:
Hypotheses Prioritization Matrix
Multiple Dimensions: Choose from Impact vs. Confidence, Impact vs. Ease, or Confidence vs. Ease
Interactive Positioning: Drag hypotheses to position them on the matrix
Priority Scoring: Automatic calculation based on position
Visual Grouping: Color-coded by validation state
Prioritization Systems
Choose the right framework
Impact vs. Confidence: High-impact, high-confidence hypotheses first
Impact vs. Ease: High-impact, easy-to-test assumptions for quick wins
Confidence vs. Ease: Build confidence with easy tests before harder ones
Advanced Features
🤖 Spell-Powered Generation
AI features for creating and managing hypotheses:
Context-Aware Generation: Create hypotheses based on personas, validation data, trials or insights
Reference Integration: Automatically link generated hypotheses to source material
📊 Scoring & Analytics
Systematic approach to hypothesis prioritization:
Impact Score: How much this assumption affects your success
Confidence Score: How certain you are about this assumption
Ease Score: How difficult it would be to test this assumption
Automatic Calculation: Priority score computed from all three dimensions
Hypothesis Dimensions
Categorize your assumptions by the type of risk they address:
Desirability: Do users want this solution?
Viability: Can we build a sustainable business around this?
Feasibility: Can we technically build this solution?
Problem-Solution Fit: Does our solution address the real problem?
Product-Market Fit: Is there sufficient market demand?
Unknown: General assumptions that don't fit other categories
Best Practices
Make assumptions explicit - Write down what you believe, even if obvious
Focus on testable statements - Ensure hypotheses can be validated or invalidated
Prioritize ruthlessly - Use scoring to focus on highest-impact assumptions first
Link to evidence - Connect hypotheses to personas, insights, and research
Update states regularly - Keep validation status current as you learn
Test early and often - Validate critical assumptions before building
Document learning - Capture what you learn whether hypotheses are validated or not
Pro Tip
The most effective hypothesis management combines systematic documentation with ruthless prioritization. Use spells to generate initial assumptions, then focus your validation efforts on the highest-priority, most critical beliefs.
Integration with Other Tools
Hypotheses work seamlessly with other platform features:
Link to Personas: Test assumptions about specific user types
Attach to Insights: Ground hypotheses in real user feedback
Design Interviews: Create structured interviews to validate assumptions
Track with Tasks: Convert validation needs into actionable work items
Workflow Examples
Assumption Mapping Workflow
Brainstorm: Write down all your assumptions about users, market, and solution
Hypotheses Section
Hypotheses Board
Hypotheses Prioritization Matrix