What independent replication contributes

The National Academies distinguish computational reproducibility, which uses the same data and procedures, from replicability, which tests the same question with new data. Both are valuable but detect different problems.

Rerunning code can show whether published figures follow from the original dataset. Repeating the experiment adds another acquisition of data. A third publication reanalyzing the same sample is not, by that fact alone, an independent experimental replication.

As a reading criterion, identify who obtained the new samples, where the work was performed and which analytical components were shared. Independence is not summarized by the first author's name: materials, decisions and data also matter.

Sharing a procedure may be necessary to answer the same question. What matters is declaring that continuity and distinguishing it from repeating errors or undocumented decisions. The comparison must reveal what stayed the same and what changed.

The Reproducibility Project: Cancer Biology documented obstacles to repeating preclinical experiments, including missing statistical data and insufficient procedural descriptions. It also found that plans needed modification during execution.

This shows why a complete publication requires more than results and figures. Without information about materials, criteria and analyses, another team may have to reconstruct decisions. Necessary modifications must be recorded to assess whether the comparison still addresses the original question.

The results article from the same project assessed replicability through several criteria. In its sample, many positive effects were smaller in replications. The different evaluation methods were not applicable to all results.

A single percentage therefore does not summarize all the evidence. Review direction, magnitude, uncertainty and experimental correspondence. A smaller effect can retain its direction while substantially changing the interpretation.

The National Academies note that one successful replication does not definitively confirm an explanation and one failure does not conclusively refute it. A discrepancy can guide new questions about system variability or design differences.

For an editorial synthesis, present both results and explain what comparison they allow. Do not replace one strong conclusion with an equally categorical opposite. Replication is valuable when it helps define the conditions under which a claim holds and the uncertainty that remains.