Structure complex questions into testable hypotheses. Use when validating product ideas, debugging problems, planning experiments, or breaking down ambiguous challenges into actionable research.
Breaks down complex problems into testable hypotheses using a MECE framework. Use when investigating why metrics are underperforming, validating product ideas, or planning experiments to prioritize what to test first.
/plugin marketplace add flpbalada/thinking-toolkit/plugin install hypothesis-tree@thinking-toolkitThis skill inherits all available tools. When active, it can use any tool Claude has access to.
A Hypothesis Tree is a structured approach to breaking down complex questions into testable hypotheses. Originally from management consulting (McKinsey), it ensures MECE (Mutually Exclusive, Collectively Exhaustive) coverage of a problem space.
Main Question
"Why is X happening?"
|
+---------------+---------------+
| | |
Hypothesis A Hypothesis B Hypothesis C
| | |
+--+--+ +--+--+ +--+--+
| | | | | |
Sub- Sub- Sub- Sub- Sub- Sub-
hyp hyp hyp hyp hyp hyp
Mutually Exclusive: No overlap between branches Collectively Exhaustive: All possibilities covered
Good MECE: Bad (not MECE):
+----------------+ +----------------+
| New users | | Mobile users | <- Overlap
|----------------| |----------------|
| Returning | | New users | <- Overlap
| users | |----------------|
+----------------+ | Some users | <- Vague
+----------------+
Strong hypotheses are:
| Element | Description | Example |
|---|---|---|
| Specific | Clear, measurable | "Checkout abandonment is >70% on mobile" |
| Testable | Can be proven/disproven | Not "users don't like it" |
| Falsifiable | Could be wrong | Has clear failure criteria |
| Actionable | Leads to decision | If true → do X, if false → do Y |
Convert vague concerns into structured questions:
| Vague | Structured |
|---|---|
| "Growth is slow" | "Why is our MoM user growth <5%?" |
| "Users aren't engaged" | "Why is D7 retention below 20%?" |
| "Feature isn't working" | "Why is feature X adoption <10%?" |
Brainstorm potential explanations, then organize MECE:
Question: "Why is signup conversion <30%?"
Level 1 Hypotheses:
├── Awareness: Users don't understand the value proposition
├── Ability: The signup process is too difficult
├── Motivation: The perceived benefit isn't worth the effort
└── Technical: Bugs/errors prevent completion
Keep breaking down until hypotheses are directly testable:
Ability: The signup process is too difficult
├── Too many fields required
├── Password requirements unclear
├── Form validation confusing
└── Mobile experience broken
| Hypothesis | Evidence Available | Test Effort | Impact if True |
|---|---|---|---|
| [Hyp 1] | [None/Some/Strong] | [L/M/H] | [L/M/H] |
| [Hyp 2] | [None/Some/Strong] | [L/M/H] | [L/M/H] |
Priority = High Impact + Low Effort + Little Existing Evidence
## Hypothesis Tree Analysis
**Central Question:** [Clear, specific question] **Date:** [Date] **Owner:**
[Name]
### Hypothesis Tree Structure
[Main Question] ├── H1: [First major hypothesis] │ ├── H1.1: [Sub-hypothesis] │
└── H1.2: [Sub-hypothesis] ├── H2: [Second major hypothesis] │ ├── H2.1:
[Sub-hypothesis] │ └── H2.2: [Sub-hypothesis] └── H3: [Third major hypothesis]
└── H3.1: [Sub-hypothesis]
### Prioritized Testing Plan
| Priority | Hypothesis | Test Method | Timeline | Owner |
| -------- | ---------- | ----------- | -------- | ----- |
| 1 | [H1.2] | [Method] | [Time] | [Who] |
| 2 | [H2.1] | [Method] | [Time] | [Who] |
### Current Evidence Summary
| Hypothesis | Status | Evidence |
| ---------- | ---------------------------- | --------- |
| [H1] | [Confirmed/Rejected/Testing] | [Summary] |
Question: "Why is our new reporting feature only used by 8% of users?"
Low Feature Adoption
├── Awareness
│ ├── Users don't know it exists
│ └── Announcement wasn't clear
├── Value
│ ├── Feature doesn't solve their problem
│ └── Existing workarounds are "good enough"
├── Ability
│ ├── Feature is hard to find
│ └── Feature is hard to use
└── Timing
└── Users don't need reports frequently
Question: "Why did monthly churn increase from 5% to 8%?"
Increased Churn
├── Product Changes
│ ├── Recent feature change caused issues
│ └── Performance degradation
├── Market Changes
│ ├── Competitor launched better alternative
│ └── Economic conditions changed
├── Customer Mix
│ ├── Acquired lower-quality leads
│ └── Channel mix shifted
└── Service Issues
└── Support quality declined
| Method | Combined Use |
|---|---|
| Five Whys | Go deep on confirmed hypotheses |
| Jobs-to-be-Done | Frame hypotheses around user jobs |
| Fogg Behavior Model | Structure behavioral hypotheses |
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