Reproducibility turns an analysis from a one-off claim into something others can check and build on.
Results that cannot be rerun cannot be verified, updated or trusted for long.
The usual test is whether someone else can get the same numbers from the same inputs.
Raw data should be kept unchanged, with every cleaning step done in code.
Snapshots or dated copies of data stop a changing source from changing results.
Code should run from start to finish without manual steps, such as clicking or copying values.
Notebooks need restarting and running from the top before results are shared.
Random steps, such as sampling, need a fixed seed so they give the same result each time.
Recording package versions, for example in a requirements file, stops software updates from changing results.
Version control, such as Git, records every change to the code and who made it.
Containers can capture the whole software setup, though they add effort.
A clear folder layout and a short readme explain how to run the work.
Reproducibility is not the same as being correct, since the same mistake can be reproduced perfectly.
In practice, teams treat a report that cannot be regenerated as a draft, not a result.
The usual mistake is fixing data by hand in a spreadsheet, a step that leaves no trace and cannot be repeated.