Quick answer: Reproducibility is not the same as correctness, but it is how a result earns the right to be checked. The minimal standard is embarrassing to skip and easy to meet: scripted analysis, version-controlled code, documented data and cleaning steps, a recorded computational environment, and a clear route from inputs to outputs. If a reviewer, coauthor, or future you cannot rerun the analysis and get the same tables and figures, the result is not fully reproducible yet.
Part 7 of a practical statistics series for researchers. If the basics are new, start with p-values, effect sizes, and confidence intervals.
Table of Contents
Start with the failure mode
The most common reproducibility failure is not exotic. It is a set of steps that live in someone’s head, a spreadsheet, a GUI, or a folder of “final” files with no record of how they were produced.
That is enough to make a result hard to check. It also makes it hard to update when the data change, the code changes, or a reviewer asks for a different cut.
A reproducible workflow exists to answer one question: if I hand this project to another person tomorrow, can they reproduce the analysis from the materials I left behind?
If the answer is no, the project is not reproducible yet.
What “minimal” should include
A minimal reproducible workflow does not need to be elaborate. It needs to be honest and complete.
At minimum, include:
- a script or scripts that run the analysis,
- the data or a clear path to the data,
- a record of cleaning and preprocessing steps,
- the analysis code that produced the reported numbers,
- the code or settings that produced the figures,
- and a note on the environment: software, package versions, and any key settings.
That is not a luxury. It is the difference between a result and a re-runnable result.
Script everything that matters
If a step matters to the result, it should be scripted.
Clicking through menus, copying cells by hand, or renaming files in an ad hoc way is fine for exploration. It is not a reliable way to produce a result someone else can rerun.
A script has several advantages:
- it records what was done,
- it can be rerun,
- it can be changed and retested,
- and it reduces silent errors from manual steps.
Even a simple script is better than an undocumented sequence of manual actions.
Keep the data path clear
Reproducibility breaks down fast when no one knows which file was actually used.
Be explicit about:
- where the raw data are,
- what changes were made to get the analysis dataset,
- which file or object the analysis code reads,
- and whether the analysis was run on a filtered, merged, or transformed version.
A common failure is to report results from a dataset that no one can identify later. That turns a statistical result into a black box.
Cleaning steps are part of the result
Data cleaning is not a side activity. It is part of the analysis pipeline.
The choices you make when cleaning can affect the results:
- which rows are included or excluded,
- how missing values are handled,
- how variables are coded,
- how outliers are treated,
- whether duplicate records exist,
- and whether dates, units, or labels are consistent.
If these choices are not recorded, the result is not reproducible even if the code runs.
A good habit is to separate raw data from cleaned data and to document every nontrivial cleaning decision.
Version control is not optional for serious work
Version control gives you a record of what changed and when.
That matters because analysis projects evolve. Code changes. Data updates. Decisions get revised. A folder full of “analysis_final.R,” “analysis_final_v2.R,” and “analysis_real_final.R” is a sign that the project is drifting, not that it is complete.
Version control helps with:
- tracking changes,
- reverting when something breaks,
- comparing versions,
- and collaborating without overwriting each other’s work.
Even a solo project benefits from this record.
Record the environment
A result can be irreproducible even when the code is perfect, if the software environment differs enough.
That is why the environment matters:
- which language or tool was used,
- which package versions were installed,
- whether a key setting or seed was used,
- and whether the analysis depended on a specific local setup.
In many projects, recording the language version and key package versions is enough to start. If the analysis depends on a particular setup, say so plainly.
Use seeds for anything random
If the analysis involves randomness, set a seed and record it.
That includes:
- random sampling,
- imputation with random draws,
- simulation,
- bootstrapping,
- and some machine-learning procedures.
Without a seed, the same code can produce different outputs on different runs. That may be fine for exploration, but it is not a reproducible final analysis.
Separate exploration from reporting
A project usually has two phases:
- exploration, where you try things, fail, revise, and look around,
- and reporting, where you freeze a specific analysis and document it.
Those phases should not be mixed in a way that makes the final result impossible to trace.
A clean workflow keeps the exploratory noise separate from the analysis that produced the reported tables and figures. That does not mean exploration is bad. It means the final product should be traceable.
Make the outputs traceable
Every table and figure in the report should be traceable to the code that created it.
That sounds strict, but it is the point. If you cannot tell which code produced a number, the number is not reproducible in any meaningful sense.
A practical way to do this is to arrange the project so that:
- raw data go in one place,
- cleaned data are generated by code,
- analysis results are produced by code,
- and figures are generated by code.
Then the report references those outputs instead of copying them by hand from one place to another.
A simple project layout
A simple layout can be enough:
- a raw data folder,
- a data-processing script,
- an analysis script or scripts,
- a folder for figures,
- a folder for outputs,
- and a README that explains how to run the project.
The exact names do not matter as much as the clarity. Someone should be able to follow the README and reach the same outputs.
What a README should say
A good README for a statistical project usually says:
- what the project is,
- where the data come from,
- how to run the analysis,
- what each script does,
- what outputs are produced,
- and any special setup steps.
If the project cannot be rerun from the README alone, the documentation is incomplete.
Reproducibility is not a substitute for good statistics
This is worth saying explicitly: a reproducible analysis can still be wrong.
You can reproducibly compute the wrong model, the wrong comparison, the wrong sample, or the wrong interpretation. Reproducibility makes errors easier to find. It does not guarantee correctness.
That is not a weakness. It is the reason reproducibility matters. It supports scrutiny. It does not replace statistical judgment.
Common failures
- Results in a spreadsheet with no record of how they were calculated.
- A script that depends on files in an unnamed folder.
- Analysis that uses “the latest” version of a file without saying which one.
- No record of cleaning or exclusion decisions.
- Hand-fresh copies of tables into a draft with no link back to the code.
- No environment or version information.
- A project that works only on one person’s machine because of hidden setup assumptions.
Each of these is fixable. Each of them also makes the result harder to trust.
A practical checklist
Before you share or finalize a statistical result, confirm you can answer:
- Can someone rerun the analysis from the materials you have?
- Is there a script for each important step?
- Is the data path clear?
- Are cleaning decisions documented?
- Are the outputs traceable to code?
- Is the environment described?
- If randomness is involved, is the seed set and recorded?
If you cannot answer yes to most of those, the project is not yet minimally reproducible.
A quick improvement plan
If a project is messy now, do not try to make it perfect. Make it traceable.
Start with:
- one script that gets the data ready,
- one script that runs the main analysis,
- one place where results are saved,
- and a short README explaining the steps.
Then add version control, environment notes, and a record of seeds or settings if needed. Small improvements accumulate quickly.
Key takeaways
- Reproducibility means the analysis can be rerun from the materials you left behind.
- The minimal standard is scripted analysis, documented data and cleaning, version control, and a recorded environment.
- Cleaning decisions are part of the result.
- Tables and figures should be traceable to code.
- Setting seeds matters for random procedures.
- Reproducibility supports scrutiny; it does not guarantee correctness.
- A messy project can be improved by making it traceable before making it perfect.
Challenge
Take a recent analysis you have done and list the exact steps another person would need to reproduce it. Where are the gaps? Which steps live only in your head, your spreadsheets, or your file names? Write a short README that closes the biggest gap.
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FAQ
It means another person can rerun the analysis from the materials you provide and get the same results, or at least the same results up to the expected randomness.
- What does reproducible analysis mean?
No. A minimal workflow is enough if it includes scripted steps, clear data handling, and enough environment information to rerun the analysis.
- Do I need a complex workflow to be reproducible?
No. A result can be reproducible and still wrong. Reproducibility makes it easier to check and correct errors.
- Is reproducibility the same as correctness?
Because analysis projects change. Version control records what changed, when, and why, which makes the project traceable and easier to recover.
- Why does version control matter for statistics?
Yes. Cleaning choices affect the dataset and the results, so undocumented cleaning makes the analysis hard to reproduce and interpret.
- Do I really need to document cleaning steps?
Write a short README that says where the data are, how to run the analysis, and which outputs correspond to which results. That alone closes a large share of common gaps.
- What is the simplest thing I can improve today?
Yes. Exploration is useful, but the final reported analysis should be traceable and reproducible, not a mix of undocumented trials.
- Should I separate exploration and final analysis?
What does reproducible analysis mean?
It means another person can rerun the analysis from the materials you provide and get the same results, or at least the same results up to the expected randomness.
Do I need a complex workflow to be reproducible?
No. A minimal workflow is enough if it includes scripted steps, clear data handling, and enough environment information to rerun the analysis.
Is reproducibility the same as correctness?
No. A result can be reproducible and still wrong. Reproducibility makes it easier to check and correct errors.
Why does version control matter for statistics?
Because analysis projects change. Version control records what changed, when, and why, which makes the project traceable and easier to recover.
Do I really need to document cleaning steps?
Yes. Cleaning choices affect the dataset and the results, so undocumented cleaning makes the analysis hard to reproduce and interpret.
What is the simplest thing I can improve today?
Write a short README that says where the data are, how to run the analysis, and which outputs correspond to which results. That alone closes a large share of common gaps.
Should I separate exploration and final analysis?
Yes. Exploration is useful, but the final reported analysis should be traceable and reproducible, not a mix of undocumented trials.