Hypothesis Tree

Hypothesis Tree

A hypothesis tree is your Day-1 best guess, structured so you can disprove it fast. Issue trees decompose the question; hypothesis trees commit to an answer and tell you what to go test.

The McKinsey discipline: don't start work without a hypothesis. Otherwise you research forever, find nothing surprising, and produce a "comprehensive overview" no one acts on.

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Issue tree vs. hypothesis tree

| | Issue Tree | Hypothesis Tree | |---|-----------|----------------| | Top | The question | The answer (your guess) | | Branches | Sub-questions (MECE problem space) | Sub-claims that, if true, prove the top claim | | Bottom | Areas to investigate | Specific tests that would kill the claim | | Use when | You don't know what matters yet | You have a strong prior and want to test it efficiently | | Risk if skipped | You miss a dimension | You waste 6 weeks on analysis that doesn't move conviction |

Both belong in the consultant toolkit. Issue tree first (frame the space), hypothesis tree second (commit and test). Skipping the hypothesis tree is the #1 reason strategy work takes 3x longer than it should.

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Design choices

• You must commit to a Day-1 answer. "I don't know" is not a hypothesis. Make a guess. Be wrong loudly and fast.
• 3 sub-hypotheses, not more. If you need 5, your top hypothesis isn't well-formed.
• Every leaf is a test that could kill the branch. "Do more research" is not a test. "Pull pricing data for 4 competitors and compare to ours" is a test.
• State conviction explicitly. Each sub-hypothesis gets a confidence (High / Med / Low). Tests focus on the low-conviction branches first — those are where you'll learn the most.
• Disconfirmation > confirmation. Design tests to kill the hypothesis, not validate it. If you can't think of what would disprove it, you're not testing — you're rationalizing.

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Structure

TOP HYPOTHESIS (your Day-1 answer to the governing question)
│
├─ SUB-HYPOTHESIS 1  [confidence: H/M/L]
│  └─ Test: [Specific analysis, named source, kill-criterion]
│
├─ SUB-HYPOTHESIS 2  [confidence: H/M/L]
│  └─ Test: [Specific analysis, named source, kill-criterion]
│
└─ SUB-HYPOTHESIS 3  [confidence: H/M/L]
   └─ Test: [Specific analysis, named source, kill-criterion]

PRIORITY: Test [low-conviction sub] first. Kill-criterion: [what result makes us abandon the top hypothesis].

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Inputs needed (interview if missing)

1. Governing question (e.g., "Should we enter the European SMB market?")
2. Your gut answer — if you had to decide today with no more data, what would you say?
3. What's the timebox — 2 weeks? 6 weeks? Drives how many tests you can run.
4. What do you already know — existing data, prior research, expert intuition.
5. What would change your mind — even before testing, name the result that would flip your answer.

If the user can't articulate a Day-1 answer, push back. Even a weak guess beats no guess.

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Process

1. Restate the governing question. Sharpen it. "Should we enter Europe?" → "Should we launch in UK + Germany SMB by end of FY?"
2. State the Day-1 answer. One sentence. Specific. Falsifiable.
3. Decompose into 3 sub-claims. If all three were true, the top claim must be true. Test the logic: if I prove all three, does the top hold? If I disprove any one, does the top fall?
4. Assign conviction. H / M / L for each sub.
5. Design tests. For each sub: what's the specific analysis, what data source, and what result would kill it.
6. Sequence by conviction. Run low-conviction tests first. They have the most information value.
7. Set a kill-criterion for the whole tree. What aggregate result makes you abandon the top hypothesis?

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Worked example

Governing question: Should we enter the European SMB market in FY26?

Day-1 Hypothesis: Yes — launch in UK and Germany only, via product-led growth (not sales), starting Q3 FY26. Skip France and Southern Europe in year one.

Tree:

TOP: Launch UK + DE SMB via PLG in Q3 FY26 (skip France / S. Europe year 1)
│
├─ SUB-1: There is real SMB demand in UK + DE  [conviction: High]
│  └─ Test: Pull search volume for our top 5 product terms in UK + DE via SEMrush.
│          Kill if monthly search volume  2.5x US or conversion <40% of US.
│
└─ SUB-3: UK + DE specifically (not France/Italy/Spain) is the right wedge  [conviction: Medium]
   └─ Test: Compare 4 dimensions across UK/DE/FR/IT/ES — payment friction (SEPA vs. local),
          language overhead (EN penetration in SMB), regulatory friction (GDPR sub-cases),
          competitor share.
          Kill if France scores higher than DE on 3 of 4 — would force re-sequencing.

PRIORITY: Run Sub-2 first (lowest conviction, highest stakes). 2 weeks, $10K. If PLG fails,
the whole top hypothesis flips to "enter via partner-led sales or don't enter."

OVERALL KILL-CRITERION: If Sub-2 fails AND we can't articulate a sales-led model with <12-month
payback, abandon FY26 entry and revisit FY27 with a partnerships path.

Note how this works: 6 weeks of work become 2 weeks of work, because Sub-2 is the load-bearing assumption. If it fails, you stop — no point testing Subs 1 and 3.

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Common mistakes to avoid

• No top hypothesis. "I want to investigate Europe" is not a hypothesis. Pick an answer.
• Sub-hypotheses that aren't load-bearing. If proving a sub doesn't change your conviction in the top, it's the wrong sub.
• Tests that can't kill the branch. "Talk to customers" isn't a test. "Run 10 customer calls; kill if 7+ say price is the blocker" is a test.
• Sequencing high-conviction first. Wastes the timebox. The whole point is to get to wrong fast.
• No kill-criterion. Without it, you'll explain away any disconfirming data. Set the bar before you see results.
• Confusing with issue tree. If your tree branches into sub-questions instead of sub-claims, it's an issue tree. Rewrite each branch as a statement.

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When to use

• Start of any strategic analysis (market entry, pricing, M&A, restructuring)
• When you've already done an issue tree and need to commit to an answer
• When the team is about to "go gather data" with no clear hypothesis
• Before any board ask that begins "we recommend..."

When NOT to use

• You genuinely don't know enough to guess (do an issue tree first)
• The question is descriptive, not decision-driving ("what is happening?" vs. "what should we do?")
• The decision is reversible and small (just try it, don't formalize)

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Pairs well with

• Issue Tree Builder — frame the problem space first, then commit to a hypothesis within it
• Synthesis — use synthesis output as input to validate or kill specific sub-hypotheses
• Decision Memo Builder — once you've tested and survived, the surviving hypothesis becomes the recommendation
• McKinsey Critic — stress-test your tree before you start running tests; cheaper to fix bad framing than bad analysis

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sruthir28
• Source: sruthir28/enterprise-ai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.