Use it when you want to begin statistical analysis of research data work without writing the first draft from scratch.
Analysis Plan Chain AI Prompt
Step 1: Primary analysis specification — specify the primary outcome, primary predictor, and the exact statistical test with its parameters (test type, alpha level, directionali... Copy this prompt template, run it in your AI tool, and use related prompts to continue the workflow.
Step 1: Primary analysis specification — specify the primary outcome, primary predictor, and the exact statistical test with its parameters (test type, alpha level, directionality). Specify this before seeing data. Step 2: Secondary analyses — list all secondary outcomes and exploratory analyses in priority order. Specify multiple comparison correction strategy. Distinguish confirmatory from exploratory analyses clearly. Step 3: Assumption checks — for each planned analysis, list all statistical assumptions and the procedure to test them. Specify in advance what will be done if each assumption is violated. Step 4: Missing data plan — specify the expected missing data mechanism, the primary handling strategy, and sensitivity analyses for alternative mechanisms. Step 5: Power analysis — calculate required sample size for the primary analysis at 80% and 90% power. Account for expected attrition. Run sensitivity analysis showing N required across a range of effect sizes. Step 6: Subgroup analyses — specify any pre-planned subgroup analyses. State the interaction test that will be used. Explicitly flag all unplanned post-hoc subgroup analyses as exploratory. Step 7: Write the statistical analysis plan (SAP) — produce a complete, timestamped statistical analysis plan that will be preregistered before data collection begins. Include: primary estimand, all analysis specifications, assumption checks, missing data plan, and planned reporting format.
When to use this prompt
Use it when you want a more consistent structure for AI output across projects or datasets.
Use it when you want prompt-driven work to turn into a reusable notebook or repeatable workflow later.
Use it when you want a clear next step into adjacent prompts in Statistical Analysis of Research Data or the wider Research Scientist library.
What the AI should return
The AI should return a structured result that is directly usable in a statistical analysis of research data workflow, with explicit outputs, readable formatting, and enough clarity to support the next step in research scientist work.
How to use this prompt
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Statistical Analysis of Research Data.
Frequently asked questions
What does the Analysis Plan Chain prompt do?+
It gives you a structured statistical analysis of research data starting point for research scientist work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for research 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?+
Analysis Plan 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 Bayesian vs Frequentist Analysis, Effect Size Interpretation, Mediation and Moderation Analysis.