The data science lifecycle is no different.ĭespite this and other processes in place to encourage robust scientific research, over the past few decades, the entire field of scientific research has been facing a replication crisis. It also makes it easier for other researchers to converge on our results. By following a shared process of how to ask and explore questions – we can ensure consistency and rigor in how we come to conclusions. The Scientific Method was designed and implemented to encourage reproducibility and replicability by standardizing the process of scientific inquiry. Without replicability, it is difficult to trust the findings of a single study. Without reproducibility, process and findings can’t be verified. If something is replicable, it means that the same conclusions or outcomes can be found using slightly different data or processes. Although there is some debate on terminology, if something is reproducible, it means that the same result can be recreated by following a specific set of steps with a consistent dataset. Reproducibility and replicability are cornerstones of scientific inquiry.
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