Experiments Repository & Evidence Bank

Turn one-off tests into shared learning: a simple operating model and template for logging experiments, outcomes, and reusable evidence across teams and projects.

What this resource helps you do

You will learn how to record experiments so future teams can understand what was tried, why, and what happened. The resource shows what metadata to capture (hypothesis, context, method, measures, sample size, dates, owners), how to grade and tag evidence, how to link experiments to decisions or initiatives, and how to preserve negative and null results so they remain useful.

Why a repository matters

Without a consistent repository, valuable learning gets scattered across notes, email, or the memories of a few people. That fragmentation causes repeated failures, duplicated effort, and slow improvement. A maintained experiments repository becomes organizational memory: it speeds onboarding, supports faster iteration, and helps teams choose better experiments based on prior evidence instead of repeating noisy pilots.

How to use this template and operating model

Use the template as a repeatable checklist for every experiment. At minimum, capture:

• Hypothesis and expected measurable outcome
• Context and scope (who, where, constraints)
• Methods and steps for reproduction
• Primary and secondary measures and collection methods
• Results, analysis, and confidence or limitations
• Decision taken and next actions (scale, pivot, stop)
• Owner, date, tags, and links to related work or data

Capture this information as structured records so others can filter, compare, and reuse findings. Where useful, convert checklists into interactive forms to collect consistent fields and store responses as structured JSON for later reporting or dashboards.

Examples across organizations

Practical uses include:

• A restaurant piloting a new menu item: record recipe, prep steps, sample nights, sales lift, customer feedback, and decision to roll out.
• A plant testing a machine-setting change: record the exact settings, batch data, yield, inspection notes, and whether the change reduced defects.
• A nonprofit A/B testing fundraising messages: record cohorts, messaging, response rates, cost per donor, and final allocation decision.
• A clinic trialing a scheduling tweak: record appointment types, wait times, no-show rates, staff feedback, and next steps.

Avoid common pitfalls

Don’t rely on screenshots, vague summaries, or private notes. Poorly documented experiments lack reproducibility and context. Use clear ownership, consistent metadata, evidence grading, and a retention policy so records remain trustworthy and useful. Make negative results explicit—knowing what didn’t work is often as valuable as what did.

How this ties to the broader Organizational Intelligence domain

This repository is a building block in a larger system for continuous improvement: it links experiments to decisions, metrics, and the organization's learning journey. Treat it as an adaptive collection you can copy and tailor to departments, sites, or initiatives—then improve the collection as new needs arise (for example, by adding forms, dashboards, or decision-links).