hypothesis-tree

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Hypothesis Tree - Structured Problem Decomposition

Hypothesis Tree - 结构化问题拆解

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.

Hypothesis Tree是一种将复杂问题拆解为可验证假设的结构化方法。它起源于管理咨询行业（如麦肯锡），确保对问题域的MECE（Mutually Exclusive, Collectively Exhaustive）覆盖。

When to Use This Skill

何时使用该方法

Validating new product or feature ideas
Investigating why metrics are underperforming
Planning user research or experiments
Breaking down ambiguous strategic questions
Prioritizing what to test first
Communicating analysis structure to stakeholders

验证新产品或功能想法
调查指标表现不佳的原因
规划用户研究或实验
拆解模糊的战略问题
优先确定测试顺序
向利益相关者传达分析框架

Core Concepts

核心概念

Structure of a Hypothesis Tree

Hypothesis Tree的结构

                    Main Question
                    "Why is X happening?"
                          |
          +---------------+---------------+
          |               |               |
     Hypothesis A    Hypothesis B    Hypothesis C
          |               |               |
       +--+--+         +--+--+         +--+--+
       |     |         |     |         |     |
     Sub-   Sub-     Sub-   Sub-     Sub-   Sub-
     hyp    hyp      hyp    hyp      hyp    hyp

                    Main Question
                    "Why is X happening?"
                          |
          +---------------+---------------+
          |               |               |
     Hypothesis A    Hypothesis B    Hypothesis C
          |               |               |
       +--+--+         +--+--+         +--+--+
       |     |         |     |         |     |
     Sub-   Sub-     Sub-   Sub-     Sub-   Sub-
     hyp    hyp      hyp    hyp      hyp    hyp

MECE Principle

MECE原则

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
                              +----------------+

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
                              +----------------+

Hypothesis Format

假设的格式

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

强有力的假设需满足：

要素	描述	示例
Specific	清晰、可衡量	"Checkout abandonment is >70% on mobile"
Testable	可被证实或证伪	避免使用"用户不喜欢它"这类表述
Falsifiable	存在被推翻的可能	有明确的失败判定标准
Actionable	能引导决策	如果成立→执行X，如果不成立→执行Y

Analysis Framework

分析框架

Step 1: Frame the Question

步骤1：明确问题

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%?"

将模糊的担忧转化为结构化问题：

模糊表述	结构化问题
"增长缓慢"	"Why is our MoM user growth <5%?"
"用户参与度低"	"Why is D7 retention below 20%?"
"功能无法正常使用"	"Why is feature X adoption <10%?"

Step 2: Generate First-Level Hypotheses

步骤2：生成一级假设

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

头脑风暴潜在原因，然后按照MECE原则整理：

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

Step 3: Decompose to Testable Level

步骤3：拆解至可验证层级

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

持续拆解，直到假设可直接验证：

Ability: The signup process is too difficult
├── Too many fields required
├── Password requirements unclear
├── Form validation confusing
└── Mobile experience broken

Step 4: Prioritize and Test

步骤4：优先级排序与测试

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

假设	现有证据	测试成本	成立后的影响
[Hyp 1]	[None/Some/Strong]	[L/M/H]	[L/M/H]
[Hyp 2]	[None/Some/Strong]	[L/M/H]	[L/M/H]

优先级 = 高影响 + 低成本 + 现有证据少

Output Template

输出模板

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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]

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]

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Hypothesis	Status	Evidence
[H1]	[Confirmed/Rejected/Testing]	[Summary]

undefined

Real-World Examples

实际案例

Example 1: Low Feature Adoption

案例1：功能使用率低

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 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

Example 2: Churn Investigation

案例2：用户流失率上升调查

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

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

Best Practices

最佳实践

Do

建议做法

Start with clear, specific question
Check MECE at each level
Get to testable hypotheses quickly (3 levels usually enough)
Update tree as evidence comes in
Share tree with stakeholders for alignment

从清晰、具体的问题入手
在每个层级检查MECE原则
快速拆解至可验证的假设（通常3层足够）
根据新证据更新Hypothesis Tree
与利益相关者共享Hypothesis Tree以达成共识

Avoid

避免事项

Overlapping hypotheses (not mutually exclusive)
Hypotheses that can't be tested
Going too deep without testing
Confirmation bias (seeking to prove favorite hypothesis)

假设重叠（不满足相互独立）
无法验证的假设
未进行测试就过度拆解
确认偏差（仅寻找支持偏好假设的证据）

Integration with Other Methods

与其他方法的结合

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

方法	结合方式
Five Whys	针对已证实的假设深入挖掘
Jobs-to-be-Done	围绕用户需求构建假设
Fogg Behavior Model	构建行为相关的假设框架

hypothesis-tree

Original

Translation

Hypothesis Tree - Structured Problem Decomposition

Hypothesis Tree - 结构化问题拆解

When to Use This Skill

何时使用该方法

Core Concepts

核心概念

Structure of a Hypothesis Tree

Hypothesis Tree的结构

MECE Principle

MECE原则

Hypothesis Format

假设的格式

Analysis Framework

分析框架

Step 1: Frame the Question

步骤1：明确问题

Step 2: Generate First-Level Hypotheses

步骤2：生成一级假设

Step 3: Decompose to Testable Level

步骤3：拆解至可验证层级

Step 4: Prioritize and Test

步骤4：优先级排序与测试

Output Template

输出模板

Hypothesis Tree Analysis

Hypothesis Tree Analysis

Hypothesis Tree Structure

Hypothesis Tree Structure

Prioritized Testing Plan

Prioritized Testing Plan

Current Evidence Summary

Current Evidence Summary

Real-World Examples

实际案例

Example 1: Low Feature Adoption

案例1：功能使用率低

Example 2: Churn Investigation

案例2：用户流失率上升调查

Best Practices

最佳实践

Do

建议做法

Avoid

避免事项

Integration with Other Methods

与其他方法的结合

Resources

参考资源