lean-ux

# Lean UX Framework

A practice-driven approach to UX that replaces heavy deliverables with rapid experimentation, cross-functional collaboration, and continuous learning. Lean UX shifts the question from "What should we design?" to "What do we need to learn?"

## Core Principle

**Outcomes over outputs.** The value of a design is measured not by the fidelity of the deliverable but by the change in user behavior it produces.

**The foundation:** Traditional UX waterfalls requirements into wireframes, mockups, specs, and code—losing context and hiding untested assumptions at every handoff. Lean UX compresses the distance between idea and evidence: declare assumptions, form hypotheses, run the smallest possible experiment, and let real user behavior settle the argument. Shared understanding replaces documentation; learning velocity replaces pixel perfection.

## Scoring

**Goal: 10/10.** Rate UX processes, design plans, or team workflows 0-10 against Lean UX principles: hypothesis-driven design, minimal deliverables, collaborative practices, and outcome-focused metrics score high; heavy-deliverable thinking or untested assumptions lower the score. Always state the current score and the specific improvements needed to reach 10/10.

## Framework

### 1. Declaring Assumptions

**Core concept:** Every design starts with assumptions. Lean UX makes them explicit so they can be prioritized and tested, rather than baked invisibly into specifications.

**Why it works:** Unspoken assumptions mean teams build on shaky ground and discover problems only after launch; surfacing them early focuses energy on the riskiest ones and reduces the cost of being wrong.

**Key insights:**
- Business assumptions define what must be true for the business (revenue model, market size, willingness to pay); user assumptions define who users are and how they behave
- Prioritize on two axes: risk (how damaging if wrong) and uncertainty (how little we know)
- Test high-risk, high-uncertainty assumptions first
- Write assumptions collaboratively as a team, not in isolation

**Product applications:**

| Context | Application | Example |
|---------|-------------|---------|
| **New feature kick-off** | Assumption mapping workshop | "We assume users want to share reports with teammates" |
| **Roadmap planning** | Rank features by assumption risk | Prioritize features whose success depends on untested beliefs |
| **Stakeholder alignment** | Expose hidden assumptions across roles | PM assumes pricing works; engineer assumes scale; designer assumes flow |

**Ethical boundary:** Assumptions must be honest assessments, not post-hoc justifications—if leadership has already committed to a direction, acknowledge the constraint rather than pretending it's open to falsification.

See: [references/hypothesis-canvas.md](references/hypothesis-canvas.md) for the assumption prioritization matrix and hypothesis statement formats.

### 2. Hypothesis Statements

**Core concept:** A hypothesis translates an assumption into a testable prediction, linking a proposed change to a measurable outcome for a specific user segment.

**Why it works:** Hypotheses force precision—instead of "make onboarding better," the team commits to a prediction that can be proven or disproven, which prevents scope creep and makes the learn step unambiguous.

**Key insights:**
- Standard format: "We believe [outcome] will happen if [persona] achieves [action] with [feature]"
- Every hypothesis specifies persona, action, outcome, and measurable signal
- Sub-hypotheses break a large bet into independently testable parts
- Agree on what "validated" and "invalidated" look like before running the experiment

**Product applications:**

| Context | Application | Example |
|---------|-------------|---------|
| **Feature design** | Write hypothesis before wireframing | "We believe trial-to-paid conversion will rise 10% if new users complete a guided setup wizard" |
| **A/B tests** | Formalize test rationale | "We believe click-through will rise 15% if we move the CTA above the fold" |
| **Sprint planning** | Attach hypothesis to each story | Story: "filter by date." Hypothesis: "task completion time drops 30%" |

**Ethical boundary:** Never cherry-pick metrics after the fact to declare a hypothesis validated—pre-commit to success criteria.

### 3. MVPs and Experiments

**Core concept:** An MVP in Lean UX is the smallest design artifact that can test a hypothesis with real users—a learning tool, not a product launch.

**Why it works:** A paper prototype tested with five users in a hallway can invalidate a hypothesis that would otherwise consume a full engineering sprint; matching experiment fidelity to assumption risk maximizes learning per unit of effort.

**Key insights:**
- Experiments range from low fidelity (paper prototypes, concierge tests) to high fidelity (coded A/B tests, Wizard of Oz)
- Choose the lowest-fidelity experiment that can answer the question
- A good experiment has a clear hypothesis, defined audience, measurable signal, and time box
- Proto-personas can stand in for full research when speed matters, but must be validated later

**Product applications:**

| Context | Application | Example |
|---------|-------------|---------|
| **Early concept validation** | Paper prototype or clickable mockup | Sketch 3 concepts, test with 5 users same day |
| **Demand validation** | Landing page smoke test | "Sign up for early access" measures real interest |
| **Usability validation** | Clickable prototype test | Figma prototype tested with 5-8 users |
| **Pricing validation** | Painted door test | Show pricing page, measure click-through before building billing |

**Ethical boundary:** Smoke tests and fake doors must not mislead users into believing a product exists—disclose test status and offer an opt-out.

See: [references/experiment-patterns.md](references/experiment-patterns.md) for experiment types, selection guidance, and design templates.

### 4. Collaborative De