Experimentation Protocols & A/B Templates

Practical templates and step-by-step protocols to plan, run, and interpret A/B tests and pilots that produce reliable, actionable evidence—not noise.

Why this matters

When organizations try changes without a clear experiment plan, they waste time, draw wrong conclusions, or miss important side effects. Well‑designed experiments answer whether a change caused the result, how large the effect is, and what unintended impacts occurred. This matters for product features, process improvements, patient workflows, operating procedures, fundraising strategies, and more.

What you'll learn and accomplish

Using these protocols and templates you will be able to:

• Write a clear hypothesis and choose an appropriate experimental design (A/B, multi‑arm, before/after, or pilot).
• Define primary and secondary metrics, map them to data sources, and create a measurement plan that captures side effects and equity considerations.
• Create a data collection template and logging checklist so observations are reproducible and auditable.
• Specify sample size or stopping rules, predefine analysis scripts or procedures, and describe how results will inform decisions.
• Recognize common threats to validity (bias, confounding, interference) and how to mitigate them in practical settings.

Who benefits

These templates are built for multidisciplinary teams who need evidence to act: product managers and engineers testing features, operations and plant teams piloting process changes, clinicians and administrators testing care pathways, researchers running field pilots, nonprofit program leads evaluating interventions, and small business owners experimenting with pricing or service changes.

Concrete examples

Practical, real‑world uses include:

• A retail team using an A/B test to compare checkout flows while tracking conversion, time-to-checkout, and customer support load.
• A hospital piloting a new triage checklist in one shift, measuring patient throughput, safety incidents, and staff workload before wider rollout.
• A manufacturer trialing a maintenance scheduling change on one production line, capturing downtime, quality defects, and operator feedback.
• A nonprofit testing two outreach scripts across different neighborhoods while measuring engagement and any differential effects across groups.

How to use the templates on this site

The resource bundle contains protocol documents, an A/B test template, and data collection forms you can copy and adapt. For teams using this platform, consider rendering the data collection templates as interactive forms to save observations and serialize responses as JSON for analysis or dashboards. If you tailor these templates to your organization, keep a versioned record of changes so results remain interpretable.

Quick checklist: design guardrails to avoid bad experiments

• Declare a primary outcome and hypothesis before looking at results.
• Document sample size, randomization method, and stopping rules.
• Map each metric to a specific data source and logging field.
• Capture secondary outcomes and potential harms or equity impacts.
• Plan who will analyze results and what decision thresholds or follow-up actions will be used.