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Analytics Method Library
A practical catalog of analysis recipes, causal-inference basics, A/B design, and synthesis patterns for reliable learning and decisions.
Analytics Method Library
Practical, repeatable analysis recipes and experiment patterns that help teams answer learning questions, test ideas, and preserve reproducible work.
Why this library matters
Organizations make better decisions when analysis is reliable, shared, and repeatable. This library helps people move from ad-hoc statistics and noisy dashboards to disciplined patterns: choose the right method, check assumptions, capture design details, and record outputs so the work can be reviewed, reused, and turned into action. It’s targeted at analysts, product managers, operations leads, improvement teams, and practitioners in service businesses, manufacturing, healthcare, education, and nonprofits who must turn questions into credible evidence.
What you will learn and be able to do
Use the library to: select an appropriate analysis recipe for common problems; sketch causal assumptions with simple diagrams; design and size A/B and quasi-experimental tests; run descriptive, diagnostic, and regression analyses with attention to bias; synthesize results into clear recommendations; and document methods so others can reproduce or extend the work. Practical examples include A/B testing a restaurant menu layout, testing a reminder message in a clinic, comparing maintenance interventions on a production line, and evaluating a fundraising email for a nonprofit.
What good practice looks like
Good practice combines method choice and discipline: articulate the learning question, define success metrics and ownership, map data sources and quality checks, state causal assumptions and pre-analysis plans, run power and sensitivity checks, document steps and results, and propose the next experiment or operational change. This resource emphasizes patterns that make analyses useful to teams—not mysterious statistical outputs but clear inputs to experiments, decisions, and continuous learning.
Platform ways to use and extend the library
The library is designed to be copied and adapted to your Hunger Engine: you can tailor method cards to local data schemas, embed checklists as interactive forms to capture pre-analysis plans or audits, and store results and forms as structured JSON for later review or dashboards. Consider packaging a subset as a toolkit for a site, plant, clinic, or program so local teams inherit consistent methods and documentation standards.
Explore the catalog to find method recipes, checklists, and example use-cases. Copy or adapt the library for your team, or use its checklists and forms as the basis for reproducible experiments and shared learning.
Make useful resources part of something bigger.
The Hunger Engine is moving toward living domains, toolkits, and collections that people and organizations can explore, acquire, tailor, extend, and improve. A useful resource can become part of a personal collection, team toolbox, site-specific domain, or shared enterprise capability.
Start with what you're hungry to improve. As your needs grow, collections can bring together knowledge, audits, forms, dashboards, data, AI, integrations, and other capabilities without requiring you to start from scratch.