Validation

Reproducibility

Reproducibility asks whether a finding can be repeated with transparent methods, auditable data, and comparable analysis decisions.

Decision summary

Use reproducibility as the validation lens when a result needs to support future work. The page should identify which parts of the method, data, and analysis must be visible for another team to repeat the result.

Protocol layer	Materials, timing, setup, assignment, handling, and scoring rules.
Data layer	Raw endpoints, exclusions, missingness, and QC records.
Analysis layer	Model, transformations, effect sizes, uncertainty, and code or settings.
Validation output	Clear boundary between repeatable evidence and interpretation.

Use when

A method, endpoint, or interpretation will guide future experiments or product choices.
The result depends on analysis decisions, exclusions, or environmental conditions.
The page needs to separate exploratory findings from repeatable evidence.

Do not use when

The protocol, raw endpoint definitions, or analysis settings are unavailable.
The claim depends on a single underpowered or poorly documented result.

Caveats

Reproducibility is not guaranteed by a familiar method name.
A repeatable endpoint can still support a weak construct claim.
Small, flexible, or poorly documented studies can look persuasive while being hard to repeat.

Reporting checklist

Report protocol details needed to repeat the work.
Report raw endpoint definitions and QC rules.
Report analysis settings, exclusions, and transformations.
Report effect sizes with uncertainty.
State which claims are supported directly and which remain interpretive.

References

Landis et al. 2012. A call for transparent reporting to optimize the predictive value of preclinical research.

Reproducibility-oriented reporting guidance for preclinical methods.

https://pubmed.ncbi.nlm.nih.gov/23060188/

Button et al. 2013. Power failure: why small sample size undermines the reliability of neuroscience.

Discusses reliability risks from low power and inflated effects.

https://pubmed.ncbi.nlm.nih.gov/23571845/

Related surfaces

Use these related surfaces to move from the scientific method question to the relevant product page, endpoint definition, analysis tool, or adjacent guide.

Effect size

Magnitude and uncertainty reporting.

Randomization

Design transparency for allocation.

Tracking quality control

Data-quality audit before endpoint interpretation.