Justify my sample size with a power analysis
Works through a power analysis with defensible effect-size assumptions and writes the sample size justification for my methods or proposal.
The prompt
Help me determine and justify the sample size for my study.
Design: [e.g. "two-group RCT", "2x2 within-subjects", "multiple regression with 5 predictors", "multilevel with students in classes"]
Primary outcome and planned analysis: [DETAILS]
Smallest effect worth detecting, and where that comes from: [prior studies, pilot data, minimal clinically or practically important difference]
Alpha, desired power, sidedness: [e.g. "0.05, 80%, two-sided"]
Expected attrition or exclusions: [PERCENT]
Practical ceiling on recruitment: [NUMBER]
Steps:
1. Check that the effect size basis is defensible; warn me about using inflated published effects or arbitrary benchmarks ("medium effect") without justification.
2. Show the calculation approach and formula or tool settings (e.g. G*Power inputs, R pwr or simr code) so I can reproduce it.
3. Give the required sample size, then inflate for attrition and design effects (clustering, multiple comparisons).
4. Provide a sensitivity table: required N across a range of plausible effect sizes.
5. If my recruitment ceiling is too low, explain options: sensitivity power analysis, a different design, a stronger manipulation, or a pilot framing.
Return the numbers, reproducible code or settings, and a 120-word justification paragraph for my methods section. Verify final numbers in the software before use.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- DETAILS
- e.g. "0.05, 80%, two-sided"
- PERCENT
- NUMBER
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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