replicability

Replicability is the extent to which a study’s results can be obtained again in new research that tests the same hypothesis using new data and typically similar methods. Reproducibility, by contrast, usually refers to obtaining the same results from the original data and analysis, given access to the data and code. Terminological variation across disciplines has led to confusion; some communities reverse the meanings, so clarity is essential when evaluating a claim.

Replicability is central to the scientific method because it tests whether findings are robust across different samples, settings, or researchers. When results fail to replicate, questions arise about statistical power, bias, or the generalizability of theories. High-profile debates about replication have highlighted concerns in several fields, though interpretations vary by discipline and context.

Factors influencing replicability include study design quality, statistical power, measurement validity, and the presence of selective reporting or publication bias. Data quality, context specificity, and the use of complex interventions can also affect whether results are replicable. Accessibility of data and analysis protocols further shapes the ability of independent researchers to test the same claims.

Efforts to improve replicability include preregistration of study plans, publicly sharing datasets and analysis code, adopting registered reports, using larger sample sizes, and conducting independent replication studies. Meta-analytic approaches can synthesize evidence across replications to assess overall robustness. Despite progress, replicability remains contingent on field-specific norms and practical constraints, so ongoing transparency and methodological standards aim to distinguish robust findings from chance results.