Reproducibility Checklist: What Would You Need to Replicate?
The ultimate credibility test for any study — assess whether a paper provides enough detail for another researcher to re-run the exact analysis and verify the results.
Why Reproducibility Matters (Even If You’ll Never Replicate)
You’re probably not going to re-run the study. So why care about reproducibility? Because it’s the best proxy for trustworthiness.
A study that provides enough detail to replicate is a study where the researchers have thought carefully about their methods. It’s a study where reviewers and readers can verify claims independently. Missing reproducibility information isn’t just an inconvenience — it’s a red flag.
The reproducibility checklist prompt turns AI into your research transparency auditor. Paste the methods section, and AI will systematically evaluate what’s provided, what’s missing, and what you’d need to verify the results yourself.
The Five Categories of Reproducibility
The prompt evaluates five critical areas:
DATA: Is the dataset publicly available? If not, what would you need to recreate or access it? Private datasets aren’t always a problem (medical records, for example), but the paper should explain why and describe the data in enough detail for others to collect similar data.
METHODS: Are procedures described step-by-step? Could you follow the methods section like a recipe? Vague descriptions like “participants completed a survey” fail this test; specific descriptions like “participants completed the 20-item PANAS scale via Qualtrics” pass.
MATERIALS: What tools, software, instruments, or resources are required? Are version numbers specified? A study using “SPSS” is less reproducible than one using “SPSS v27” because software updates can change results.
CODE: Is analysis code shared (via GitHub, OSF, or supplementary materials)? If not, is the analysis described precisely enough to recreate? Statistical modeling choices often determine results — sharing code removes ambiguity.
ANALYSIS PIPELINE: Can you trace the path from raw data to final results? Are data cleaning steps documented? Are exclusion criteria explicit? Missing steps in the pipeline are where errors and p-hacking hide.
After running the checklist, ask AI: “Which missing items would most prevent replication? Rank them by severity.” This helps you focus on the most critical gaps.
How to Interpret the Checklist
The prompt generates a rating for each category:
✅ Fully provided: Another researcher could replicate this aspect without guessing or contacting the authors.
⚠️ Partially described: Some information is provided, but gaps remain. You might be able to approximate the procedure, but not replicate it exactly.
❌ Missing or unclear: Critical information is absent. Replication would require significant guesswork or direct communication with authors.
DATA: ⚠️ Data not shared publicly, but sample characteristics are detailed and recruitment procedures are clear enough to collect comparable data.
METHODS: ✅ Full experimental protocol with randomization procedure, timing, and stimulus descriptions.
MATERIALS: ⚠️ Software mentioned (Python, R) but version numbers not specified. Stimulus materials not included.
CODE: ❌ No code shared. Statistical tests named but specific model specifications not provided.
ANALYSIS PIPELINE: ⚠️ Data exclusion criteria stated, but data cleaning steps not documented.
A perfect checklist score doesn’t mean the study is correct — only that it’s transparent. Conversely, missing information may have legitimate reasons (privacy, proprietary tools). Use the checklist as one input, not a verdict.
Build Your Research Evaluation Stack
The Reproducibility Checklist works alongside:
Limitations & Assumptions — Find what the paper admits and what it doesn’t
Methods Decoder — Understand what the study did before evaluating transparency
Related Work Finder — Find replications and contradictory studies
Frequently Asked Questions
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