Real-World Examples

What You’ll Find Here

This section features real-world examples from leading companies demonstrating human factors principles in action. Each example includes:

The Principle - Core concept being applied
Implementation Details - Specific design decisions and rationale
Evidence Boundary - What primary sources establish and what remains a design inference
Measurement Approach - Published outcomes when available; otherwise, a concrete evaluation plan
Human Standards Connection - Links to our documentation and MCP validation rules
Code Examples - Practical implementations you can adapt

Why Examples Matter

Theory is valuable, but seeing principles applied makes them actionable. These examples show:

Product Evidence - Primary documentation for the behavior being analyzed
Design Decisions - The “why” behind implementation choices
Measurement Discipline - How to test whether the pattern improves outcomes
Implementation Patterns - Code you can adapt for your projects

Examples by Category

Cognitive Load Reduction

Progressive Disclosure - TurboTax

How a guided tax workflow can stage complex information and decisions.

Read case study →

Smart Defaults - iOS Camera

How iPhone Camera automates focus, exposure, HDR, and low-light capture while preserving optional controls.

Read case study →

Defensive Design & Error Prevention

Defensive Design - Gmail Undo Send

How Gmail’s “Undo Send” feature prevents email mistakes and reduces regret through forgiveness over permission.

Read case study →

Error Prevention - Grammarly

How Grammarly documents inline, categorized suggestions with explanation and accept/dismiss controls.

Read case study →

Accessibility & Inclusive Design

Keyboard Navigation - BBC

What BBC’s published GEL guidance establishes about focus, semantics, bypass controls, and dynamic components.

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Feedback & System Status

System Feedback - Stripe Dashboard

How Stripe documents transaction status, decline details, refund actions, event delivery, and reporting.

Read case study →

Coming Soon

More examples are being added regularly. Future categories include:

Contextual Help - In-context guidance (Slack)
Recognition Over Recall - Visual browsing (Spotify)
Consistency - Platform standards (Apple HIG, Material Design)
User Control - Customization (YouTube playback controls)
Affordances - Visual cues for interaction (Material Design)
Data Visualization - Dashboard design (Grafana, Datadog)

Using Examples with Human Standards MCP

Each example shows how to query the Human Standards MCP server for guidance. The MCP acts as a reference book that AI tools consult while building interfaces:

// Example: Building a complex form - query relevant heuristics
const errorPrevention = await mcp.callTool('get_heuristic', { id: 'H5' });
// Returns: Error Prevention - validation, confirmations, constraints

const errorRecovery = await mcp.callTool('get_heuristic', { id: 'H9' });
// Returns: Help users recover from errors - clear messages, solutions

// Search for form-specific patterns in Human Standards docs
const formDocs = await mcp.callTool('search_standards', { query: 'forms' });
// Returns: Form design patterns, validation timing, cognitive load

The MCP server encodes proven patterns from Nielsen’s 10 Usability Heuristics and Human Standards documentation, allowing AI tools to look up relevant principles based on context.

How to Apply These Patterns

1. Before Building

Read the example to understand the principle
Check the evidence boundary to separate documented behavior from interpretation
Query the MCP server for component-specific guidance

2. During Implementation

Use the code examples as starting points
Follow the design decisions documented in the case study
Reference Human Standards docs for deeper understanding

3. After Building

Validate with MCP to check compliance
Measure metrics to quantify your impact
Test with users to confirm improvements

Metrics Guide

The following are hypothetical formats for planning your own evaluation—not benchmarks from the products in these case studies:

Metric Type	What It Measures	Example
Task Completion Rate	% of users who successfully complete task	68% → 87% (+19pp)
Time on Task	How long tasks take	45 min → 31 min (30% faster)
Error Rate	Frequency of user mistakes	15% → 5% errors
Help Requests	% of users needing support	18% → 10% (-45%)
User Satisfaction	Subjective experience rating	3.2/5 → 4.4/5 (+38%)
Conversion Rate	% who complete desired action	2.5% → 3.8% (+52%)

Define a baseline, sample, task, collection method, and uncertainty before interpreting a change. Do not reuse the example values as expected outcomes.

Source Attribution

Many examples in this section are adapted from the excellent Luxor UX Principles skill by Manu Tej + Claude Code, licensed under MIT. We’ve enhanced them with:

Human Standards documentation cross-references
MCP server validation rule connections
Implementation code examples
AI code generation context

See THIRD_PARTY_LICENSES.md for full attribution.

Contributing Examples

Have a great example of human factors principles in action? We’d love to include it!

How to Document Examples

Complete framework for creating compelling case studies with before/after comparisons, hypothesis testing, metrics, and transferable lessons.

Read the full guide →

Quick Requirements:

✅ Specific problem with quantitative evidence
✅ Clear hypothesis based on human factors principles
✅ Implementation details with screenshots/code
✅ Measured impact with sample sizes, or an explicit statement that no outcome evidence is available
✅ Inline primary sources for externally verifiable claims
✅ Lessons learned and trade-offs
✅ Connection to Human Standards documentation

Submission:

Review the documentation guide
Fork the repository
Create a new file in src/content/docs/examples/
Use existing examples as templates
Submit a pull request

See CONTRIBUTING.md for general guidelines.

Browse all examples using the sidebar navigation, or jump to a specific category above.