Use case 01
A new experiment is being designed and needs a rigorous brief.
Data ScientistExperimentationAdvancedChain
Full Experiment Design Chain AI Prompt
This prompt designs an experiment from first principles before any users are exposed. It is useful for pre-registration, alignment with product and business stakeholders, and avoiding weak experiment setups. The chain covers hypothesis, sample size, randomization, guardrails, and decision rules.
Prompt text
Step 1: Define the experiment — what hypothesis are we testing, what is the primary metric, what is the minimum detectable effect, and what is the business rationale? Step 2: Calculate sample size — given baseline metric, MDE, α=0.05, power=0.80. Calculate required experiment duration based on available traffic. Step 3: Design the assignment — define unit of randomization (user, session, device). Check for network effects or contamination risks. Define the holdout strategy. Step 4: Define guardrail metrics — list 3–5 metrics that must not degrade. Define the threshold for each guardrail. Step 5: Design the analysis plan — specify the primary statistical test, multiple testing correction method, and pre-registration of hypotheses. Step 6: Write the experiment brief: hypothesis, primary metric, guardrail metrics, sample size, duration, assignment method, analysis plan, decision criteria for ship/no-ship.
When to use this prompt
Use case 02
You want the statistical plan agreed before launch.
Use case 03
Guardrails and assignment risks must be defined up front.
Use case 04
You need a reusable experiment design template for the team.
What the AI should return
A complete experiment brief covering hypothesis, metrics, MDE, sample size, duration, assignment method, guardrails, analysis plan, and ship/no-ship decision criteria.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Experimentation.
Frequently asked questions
What does the Full Experiment Design Chain prompt do?+
It gives you a structured experimentation starting point for data scientist work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for data scientist workflows and marked as advanced, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Full Experiment Design Chain is a chain. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are A/B Test Analysis, Bayesian A/B Analysis, Causal Inference Analysis.