TABLE OF CONTENTS
Assumption Testing: A Strategic Framework for Successful Initiatives
Stop launching major projects and initiatives based on risky guesses. Learn how the Assumption Testing strategic framework helps you validate ideas in 3 phases, build confidence, and ensure project success.
This is part of a series on problem solving for high-impact innovations
1. Strategy & Approach
AI Strategy: Case Study of How We Helped a $6 Billion Foundation
Philanthropic Strategy: Three Easy-to-Miss Tips for Impact (One Pager)
2. Decision-Making & Risk
Streamlining Project Initiation: An Essential Checklist
Assumption Testing: A Strategic Framework for Successful Initiatives
The Regret Minimization Framework: A Practical Checklist
What is a Calculated Risk? An Essential Guide
Strategic Decision Making Framework: Don’t Miss This Essential Tool
3. Goals and Metrics
Tech-Enabled Services Versus SaaS Business Model: Key Metrics
How To Achieve Financial Sustainability Without Sacrificing Impact
Founder Market Fit: The Overlooked Path to Your Ideal Client Profile
Tech-Enabled Services Versus SaaS Business Model: Key Metrics
How To Achieve Financial Sustainability Without Sacrificing Impact
Founder Market Fit: The Overlooked Path to Your Ideal Client Profile
From Risky Guesses to Validated Plans with Assumption Testing
Launching a new initiative often feels like navigating fog. We base plans on assumptions – about what users want, what technology can do, what the market needs. Proceeding without assumption testing is risky, leading to wasted resources and failed projects.
The Assumption Testing Framework, specifically our 3-Phase approach, provides a structured way to clear the fog by systematically validating our riskiest assumptions first. What makes this framework distinct is its combination of a systematic risk focus across all domains (value, feasibility, etc.) with a clear, actionable process (Identify & Prioritize -> Test -> Learn & Adapt), offering a robust guide to building on validated learning.
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Prerequisite: Your Initial Lean Blueprint
This assumption testing framework begins once you have drafted your Lean Blueprint – your initial hypothesis for the simplest possible V1 (minimum essential scope, most direct path to value, effortless initial engagement). This lean blueprint created by streamlining project initiation defines what you intend to validate.
Running Example: Apex & The AI Tool
Before starting formal assumption testing, Apex, a social impact firm, used streamlining principles to draft their initial Lean Blueprint for the AI tool. Their blueprint hypothesized:
- Min Scope: Version 1 (V1) will generate draft “vision” statements only from the “Vision” section of the worksheet. This section contains the client’s raw inputs and notes about what they believe their vision is.
- Shortest Path: Use a basic AI interface (like ChatGPT), team uploads only a tiny piece of text, and reviews the corresponding draft.
- Effortless Engagement: Team member spends <5 mins per worksheet section.
This simple blueprint became the starting point for identifying assumptions in Phase 1.
Overview of the Assumption Testing
ㅤ | Phase | Core Question Answered | Primary Deliverable | Main Risk Avoided |
1 | Identify & Prioritize for Assumption Testing | What are our riskiest beliefs? | Ranked Assumption Backlog | Wasted effort testing low-impact assumptions |
2 | Design & Run Minimal Assumption Testing | How can we test the top risk fastest? | Test Design & Validation Findings | Building based on flawed core assumptions |
3 | Learn, Adapt & Iterate from Assumption Testing | What did we learn & what's the next right step? | Learning Summary & Adapted Plan/Test | Continuing down the wrong path |
Phase 1: Identify & Prioritize for Effective Assumption Testing
- Phase Goal: Surface hidden beliefs underpinning the project and focus attention on the most critical uncertainties first through structured assumption testing preparation. This avoids wasting time testing low-risk assumptions. We answer "What are our riskiest beliefs?" by brainstorming all requirements and assumptions, then ranking them by potential impact and our confidence level.
Deliverable | Why It Matters |
Ranked Assumption Backlog | Creates clear, shared understanding of potential failure points. Prioritization ensures assumption testing focuses on what matters most first. |
- Execution: To produce this deliverable, the team follows these steps:
ㅤ | Team Action | Client/Sponsor Action Needed |
1 | List Beliefs: Brainstorm key project Requirements and identify the underlying Assumptions they depend on (technical, user, value, process). Remember requirements often mask untested assumptions needing validation. | (Provide initial project brief/goals) |
2 | Assess Risk for Assumption Testing: Evaluate each assumption's potential Impact if wrong and your current Confidence Level (Low/Medium/High) about it. Use Risk ≈ Impact x Lack of Confidence to guide prioritization for assumption testing. | (Offer insights on impact/confidence from stakeholder perspective, if applicable) |
3 | Prioritize Focus for Assumption Testing: Rank the assumptions based on risk to identify the top 1-3 highest risks (High Impact + Low Confidence) to test immediately. | (Align on top priorities for validation) |
Running Example: Phase 1 - Preparing for Assumption Testing at Apex
Apex held an "Assumption Storming" session for their AI tool.
- Listing Beliefs: They listed Requirements like "Generate narrative pillars" and Assumptions like "Users will adopt," "AI can parse inputs," and critically identified the assumption hidden in the requirement: "Assumption: Generating polished narratives (Requirement) is achievable via simple AI text extraction (Assumed Mechanism)."
- Assessing Risk: They rated this mechanism assumption as High Impact (core function failure) and Low Confidence (unproven with their specific inputs/desired outputs).
- Prioritizing Focus: This assumption ranked as the #1 highest risk. It became the top item on their Ranked Assumption Backlog, dictating the focus for their first round of assumption testing in Phase 2.
Phase 2: Design & Run Minimal Assumption Testing Experiments
- Phase Goal: Generate reliable data to validate or invalidate the highest-risk assumption using the fastest, cheapest method possible, applying practical assumption testing. This directly tackles the danger of building significantly based on flawed core beliefs. We answer "How can we test the top risk fastest?" by designing and executing the leanest possible experiment, aiming to efficiently climb the 'Evidence Quality Ladder'.
ㅤ | Deliverable | Why It Matters |
ㅤ | Test Design & Validation Findings | Provides concrete evidence from assumption testing about the riskiest belief. Ensures learning is based on data, preventing major strategic errors. |
- Execution: To produce this deliverable, the team follows these steps:
ㅤ | Team Action | Client/Sponsor Action Needed |
1 | Design Test (Using Evidence Quality): Create the Minimum Viable Test (MVT) for the top assumption. Aim for a test that moves you up one or two rungs on the Evidence Quality Ladder (e.g., from gut feel to secondary data, or interviews to a small prototype test) using the least resources possible¹. | (Provide specific inputs needed for test) |
2 | Execute Test: Run the experiment objectively and collect the resulting data for the assumption testing process. | ㅤ |
3 | Analyze Findings: Objectively analyze the data to determine if the assumption testing validated, invalidated, or refined the assumption based on evidence. | (Review findings for awareness) |
Running Example: Phase 2 - Executing Assumption Testing (Revealing the Input/Output Gap
Apex's top assumption was: "Simple extraction mechanism can produce the desired synthesized output quality." Their current evidence level was low (gut feel/hypothesis). They needed an MVT to climb the evidence ladder.
- Test Options Considered:
- They considered mitigation levers to make tests feasible: limit Scope (e.g., smaller sample, fewer features tested), reduce Commitment (e.g., letter of intent instead of full contract), improve Accessibility (e.g., test with easier-to-reach proxy groups)
- Option A (Higher Effort): Build a functional prototype integrating with worksheet uploads. (High evidence quality, but high effort/cost).
- Option B (Lower Effort - MVT): Simulate extraction using a basic AI workbench with copy-pasted text. (Medium evidence quality, very low effort/cost).
- MVT Design Choice: They chose Option B (Simulation) as the MVT. It represented moving significantly up the evidence ladder (from gut feel to direct observation of the mechanism's output) with minimal resource investment, aligning with the principle of the fastest, cheapest test for the current riskiest assumption.
- Execution & Findings: They ran the simulation using 'Vision' text (Input provided by Sponsor) and compared it to the desired output. The findings clearly showed the output lacked required synthesis, demonstrating the Input-Output Gap. Assumption invalidated by this targeted MVT.
Phase 3: Learn, Adapt & Iterate from Assumption Testing Results
- Phase Goal: Translate the learning from assumption testing into concrete action, ensuring the project adapts based on evidence. This prevents continuing down a flawed path. We answer "What did we learn & what's next?" by analyzing findings, deciding to pivot or persevere, and defining the next cycle.
ㅤ | Deliverable | Why It Matters |
ㅤ | Learning Summary & Adapted Plan/Test | Explicitly closes the learning loop from assumption testing, ensuring data translates into better strategy. Prevents repeating mistakes or ignoring critical validation results. |
- Execution: To produce this deliverable, the team follows these steps:
ㅤ | Team Action | Client/Sponsor Action Needed |
1 | Synthesize Learning: Clearly summarize the key insight gained from the assumption testing validation finding in Phase 2. | ㅤ |
2 | Decide Path: Based on the learning, consciously decide: Persevere (plan next step if validated) or Pivot (change approach/assumption if invalidated). Potential actions include seeking Client Validation on pivot after assumption testing. | (Provide input on pivot decisions if needed) |
3 | Define Next Cycle: Outline the next adapted plan, the next assumption to test (based on the updated backlog), or potential descoping options revealed by the assumption testing. | (Align on next steps/priorities) |
Running Example: Phase 3 - Apex Adapts Based on Assumption Testing
Apex reviewed the Phase 2 MVT findings.
- Learning Summary: "Our assumption testing shows simple extraction is insufficient; the desired output requires synthesizing information from multiple inputs."
- Decision: They decided to Pivot their technical approach. (They consulted the sponsor to validate this strategic pivot). They brainstormed alternatives, landing on exploring a template-guided synthesis approach as more promising.
- Next Cycle Defined: Their Adapted Plan now identified a new top-priority assumption: "A template-guided prompt can produce the desired synthesized output." The next MVT would focus specifically on testing that, starting the assumption testing cycle anew.
Confident Strategy Through Assumption Testing
Untested assumptions are a major threat. By systematically identifying risks based on Impact and Confidence (Phase 1), executing focused assumption testing efficiently (Phase 2), and adapting based on evidence (Phase 3), the Assumption Testing Framework provides a powerful way to navigate uncertainty. It ensures you validate underlying assumptions before committing extensive resources.
The power of this framework lies in its core principle: always validate the assumptions posing the highest risk (Impact x Lack of Confidence) first, before committing significant resources based on that belief. Early on, these high-risk assumptions often relate to core value or feasibility ("Is this worth doing?", "Can it even work?"). As the project progresses and foundational assumptions become validated, the highest risks naturally shift towards specific implementation details ("Can we build feature X efficiently?", "Will users understand this workflow?"). The key is the continuous cycle driven by this risk-prioritization principle: always identify your biggest uncertainty, test it efficiently, learn, and adapt your plan accordingly, ensuring validation efforts consistently target what matters most.
Assumption Testing FAQs
What is the main goal of assumption testing?
The main goal of assumption testing is to reduce project risk and prevent wasted resources by identifying and validating your most critical, unproven beliefs before making significant commitments. Instead of building a full plan on shaky ground, you run quick experiments to confirm if your core ideas about users, technology, or the market are actually true, allowing you to adapt based on evidence.
- Example: Testing the assumption that customers will pay for a feature before spending months building it prevents building something nobody buys.
How does assumption testing fit with Agile or Lean methods?
Assumption testing is highly complementary to Agile and Lean. While Agile focuses on iterative delivery and responding to changing requirements, assumption testing focuses on validating the underlying hypotheses that justify those requirements or the chosen technical approach before or during development sprints. It provides the validated learning needed to ensure the Agile backlog contains valuable, feasible items and helps guide Lean principles like minimizing waste.
- Example: Before an Agile sprint to build a new onboarding flow, assumption testing might involve a quick user test on a mockup to validate the flow's usability assumption.
What's the difference between an assumption and a requirement in assumption testing?
In assumption testing, a requirement is often a stated need or feature (e.g., "The system must generate narrative reports"). An assumption is the underlying belief that must be true for the requirement to be feasible or valuable (e.g., "Assumption: AI can automatically synthesize accurate narratives from our current data to meet this requirement"). Assumption testing involves explicitly listing requirements and then digging deeper to identify and validate the hidden assumptions they rely on.
- Example: Requirement: "Mobile access." Assumption: "Our target users primarily need/prefer mobile access over desktop."
How do you prioritize which assumptions to test first?
Prioritization in assumption testing focuses on risk, typically calculated by considering two factors: 1) The potential Impact if the assumption is wrong (how badly would it hurt the project?), and 2) Your current Confidence Level (or Uncertainty) about whether the assumption is true. Assumptions with High Impact and Low Confidence are the riskiest and should generally be tested first to address potential "showstoppers" early.
- Example: An assumption about core technical feasibility (High Impact, Low Confidence) would likely be tested before an assumption about a minor UI preference (Low Impact, Medium Confidence).
What is a Minimum Viable Test (MVT) for assumption testing?
A Minimum Viable Test (MVT), sometimes called a Riskiest Assumption Test (RAT), is the simplest, fastest, cheapest experiment you can possibly run to get just enough evidence to learn about your highest-priority assumption. The goal isn't perfection or building a product; it's maximizing learning speed while minimizing effort. MVTs can range from simple surveys or interviews to paper prototypes or basic technical simulations.
- Example: To test the assumption "Users understand icon X," an MVT could be simply showing the icon to 5 target users and asking what they think it does, not building it into software.
How does assumption testing relate to solution evaluation?
This framework guides you to validate the assumptions posing the highest risk (Impact x Lack of Confidence) at each stage before committing significant resources. Early on, these often relate to core value or feasibility ("Is this worth doing?", "Can it even work?"). As the project progresses and foundational assumptions become validated, the highest risks might shift towards specific implementation details ("Can we build feature X efficiently?", "Will users understand this workflow?"). The key is the continuous cycle: always identify your biggest uncertainty, test it efficiently, learn, and adapt your plan accordingly, ensuring you consistently focus validation efforts where they matter most.
How does assumption testing relate to streamlining project initiation?
This framework is a core engine powering Streamlined Project Initiation. While streamlining focuses on designing a simple start, assumption testing provides the method for making validated choices to achieve that simplicity based on evidence.
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