Template

Applied Research & Experiment Template (Hypothesis, Design, Metrics)

A structured, fillable template to design small, ethical, and measurable experiments. Collects hypothesis, rationale, design, metrics, sample/scope, data and analysis plans, owners, timeline, decision criteria, a short pre-mortem, and ethical checks. Saves a reproducible experiment record for later review and analysis.

Interactive Tool

Applied Research & Experiment Template (Hypothesis, Design, Metrics)

This template helps you turn an idea into a well-specified, measurable experiment that produces actionable learning. Complete each section and use the pre-mortem and ethical checks before you run the experiment. Saved submissions are stored as structured JSON so you can track, compare, and learn from experiments over time.

A short, descriptive name to identify this experiment.
State the hypothesis or core question you want to answer. Be specific about expected direction and effect.
Explain why this experiment is worth running and how the result will influence decisions or operations.
Name the single main metric you'll use to judge success (include units). Keep it measurable and tied to the hypothesis.
Define the numeric or categorical threshold and direction that will count as success, and the minimum confidence or evidence you require to act on the result.
Describe exactly what you will do, step by step, so the intervention can be replicated.
Describe the baseline or comparison group/process. If none, explain why (e.g., pilot, one-group pre/post).
Choose the design that best fits your context. Randomized designs provide stronger causal evidence when feasible.
Give the planned sample size, number of groups, or scope (e.g., teams, days). Note how you estimated this or whether you will run a pilot first.
List data sources, instruments, frequency, responsible collectors, storage location, and how you'll ensure data quality.
Specify the statistical tests, aggregation rules, handling of missing data, subgroup analyses, and timelines for interim checks. Pre-specify rather than decide after seeing results.
Who is accountable (owner) and who supports (data, analysis, operations, ethics)? Include names and roles.
List teams or leaders who should be informed about the experiment and its potential outcomes.
Use YYYY-MM-DD. If exact date unknown, give a best estimate or week range.
Use YYYY-MM-DD or estimated duration (e.g., 8 weeks).
List plausible failure modes, likelihood or impact, and mitigation steps. This helps reduce predictable risks before starting.
Rate overall risk exposure based on your pre-mortem.
1.0 10.0
Identify relevant ethical or regulatory considerations. If any are selected, provide details in the Ethical Notes field.
Describe consents, approvals, anonymization steps, data retention, and who approved the experiment. Attach or link to consent forms or IRB approvals where applicable.
If results meet the success criteria, what will you do? If not, what are the next steps or follow-up experiments? Be specific about who decides and how.
How will findings be documented, shared, and incorporated into practice? Include where the experiment record and data will be stored.
By selecting 'Yes' you confirm this experiment follows applicable ethical guidelines, data protection rules, and any required approvals have been obtained or are in process.
You can explore this tool now. Sign in or create an account to save your responses and return to them later.
Sign In to Save Create an Account
Make this tool part of your work

Save a personal copy, bring it to your team, or tailor the questions and workflow to fit what you are hungry to improve.

Member customization and team collaboration are coming soon.
Discuss Ask the Librarian

Discussion

Comments and conversation will live here.