Replicable

Replicable is an adjective used to describe something that can be replicated or duplicated. In research and professional practice, objects of replication include experimental results, analyses, models, and artifacts such as code and data sets. Being replicable implies that others can obtain the same or very similar outcomes using the same procedures or materials.

In scientific contexts, replicability is often discussed alongside reproducibility. Many sources distinguish the two: replicability refers to obtaining consistent results when an independent researcher repeats the study or experiment with new data; reproducibility refers to obtaining the same results by re-running the original analysis on the original data. However, usage varies by field, and some outlets treat the terms as synonyms, which can cause confusion.

Enhancing replicability involves transparent reporting, preregistration of methods, sharing data and code, and using clear, well-documented procedures. Researchers may publish detailed protocols, provide access to materials, and predefine statistical analyses to reduce flexibility that could yield divergent results.

In computational or data-intensive work, replicable work means that others can run the same code with the same inputs to reproduce outputs, or apply the code to new data and observe similar conclusions. Tools such as version control, environment specifications, and containerization support replicability.

Limitations exist: even with high replicability, differing contexts, populations, or measurement conditions can lead to different results; irreproducibility can also reflect genuine limitations of models or overfitting. The focus on replicability is part of broader efforts to improve reliability, credibility, and cumulative knowledge across disciplines.