Examples
Worked examples
- Is an instance
Two laboratories independently producing the same Western-blot band pattern from the same antibody lot.
- Is an instance
A field protocol producing comparable soil microbiome measurements across two seasons.
Counter-examples
Looks similar, but isn't
- Not an instance
Re-running the same statistical analysis on the same data file.
- Not an instance
A literature review.
Editorial commentary
Empirical reproducibility is the ability to obtain consistent observations when an empirical procedure — a laboratory protocol, a field survey, a physical measurement — is independently repeated under matched conditions. It is the reproducibility family’s non-computational member: where computational reproducibility and results reproducibility ask whether re-running deposited code and data recreates published numbers, empirical reproducibility asks whether redoing the actual physical or observational work — new reagents, a new cohort, a new instrument run — gives a consistent answer.
Why it fails for different reasons than computational reproducibility
Computational reproducibility typically fails for mundane, fixable reasons: a missing file, an undocumented software version, an unfixed random seed. Empirical reproducibility fails for reasons that are often not fixable by better documentation alone — reagent lot-to-lot variability, uncontrolled environmental conditions, antibody specificity that differs across suppliers, cell-line authentication drift, or genuine biological variability that a single original study underestimated. This is why empirical reproducibility is the harder, more resource-intensive problem to address: it requires redoing the work, not just re-running a script, and a failure doesn’t necessarily mean the original result was wrong — sometimes it means a real but unstated boundary condition wasn’t controlled for in either study.
Where this is tracked systematically
Empirical reproducibility is the principal concern of large-scale replication initiatives in bench science, most notably the Reproducibility Project: Cancer Biology, which attempted to replicate experiments from high-impact cancer-biology papers and found mixed, often only partial, agreement with original results — a widely cited data point in the broader “reproducibility crisis” discussion (see reproducibility crisis).
Sources
Goodman, Fanelli & Ioannidis, “What does research reproducibility mean?” Science Translational Medicine (2016); Errington et al., eLife Reproducibility Project: Cancer Biology.
Also known as
wet-lab reproducibility · experimental reproducibility
Machine-readable encodings
Use in your systems
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