Service Design Tools

Hypothesis Generation

A systematic approach to creating testable assumptions about user behavior, needs, and service solutions that can be validated through research and experimentation.

Duration
1 hour
Group Size
4-6
Category
Service Design Tools
Difficulty
Easy

  • Convert assumptions into testable hypotheses

  • Structure thinking for experimental validation

  • Identify critical assumptions that need testing

  • Plan systematic learning and validation

  • Reduce risk by testing before building

  • Enable evidence-based design decisions

  • Create framework for iterative learning


  • Hypotheses generated

  • Testable assumptions

  • Foundation for experimentation


  • Make hypotheses specific and measurable

  • Focus on riskiest assumptions first

  • Design experiments that can prove hypotheses wrong

  • Keep test costs proportional to risk

  • Document all assumptions, not just obvious ones

  • Update hypotheses as you learn

  • Share learnings across team regularly

  • Use results to inform next design decisions


  1. Identify key assumptions about:


  • User behaviors and needs

  • Solution effectiveness

  • Market conditions

  • Implementation feasibility


  1. Structure assumptions as testable hypotheses:


  • "We believe that [assumption]"

  • "Because [reasoning]"

  • "We will know this is true when [measurable outcome]"


  1. Prioritize hypotheses by:


  • Risk level if wrong

  • Confidence level

  • Cost to test


  1. Design experiments to test hypotheses

  2. Define success metrics and criteria

  3. Plan data collection methods

  4. Execute tests systematically

  5. Analyze results against hypotheses

  6. Update assumptions based on learning

  7. Generate new hypotheses as needed

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For Facilitators

  • Review participant profiles and expectations
  • Prepare all materials and supplies
  • Test technology and room setup

For Participants

  • Complete pre-session survey
  • Review background materials
  • Prepare examples or case studies

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  • Hypothesis template framework

  • Assumption mapping materials

  • Prioritization tools (risk/confidence matrix)

  • Experiment planning templates

  • Research and testing resources

  • Data collection tools

  • Analysis and documentation materials

  • Progress tracking systems

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