Reproducibility and Replicability in a Fast

Methodological developments and software implementations are progressing at an increasingly fast pace. The introduction and widespread acceptance of preprint archived reports and open-source software have made state-of-the-art statistical methods readily accessible to researchers. At the same time, researchers are increasingly concerned that their results should be reproducible (i.e., the same analysis should yield the same numeric results at a later time), which is a basic requirement for assessing the results’ replicability (i.e., whether results at a later time support the same conclusions). Although this age of fast-paced methodology greatly facilitates reproducibility and replicability, it also undermines them in ways not often realized by researchers. This article draws researchers’ attention to these threats and proposes guidelines to help minimize their impact. Reproducibility may be influenced by software development and change over time, a problem that is greatly compounded by the rising dependency between software packages. Replicability is affected by rapidly changing standards, researcher degrees of freedom, and possible bugs or errors in code, whether introduced by software developers or empirical researchers implementing an analysis. This article concludes with a list of recommendations to improve the reproducibility and replicability of results