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Reproducibility, also known as repeatability and replicability, is a major principle that is essential to the scientific method. To expect reproducibility is to get results obtained by an experiment or in a statistical analysis of a data set or an observational study that should be achieved again with a very high degree of reliability when the study is replicated. There are different kinds of replication but generally, replication studies involve various researchers making use of the same methodology. Only after several such successful replications should a researcher come to be considered scientific knowledge.

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Looking at it from a narrower scope, reproducibility has also been introduced in computational sciences: The results should be documented by making all the data and code available in such a way that the computations can be executed with identical results.

Reproducibility and repeatability are related and used synonymously, but they are often usefully differentiated in more precise ways, as follows.

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Two main steps are naturally distinguished with respect to the reproducibility of experimental or observational studies – when new data presents itself in the attempt to achieve it, the term replicability is often used, and the new study is a replication of the old one. When obtaining the same results by analyzing the data set of the original study once again with the same procedures, the term reproducibility is used in a narrow, technical sense when coming to its use in computational research. Repeatability is related to the precision of the experiment within the same study conducted by the same researchers. Reproducibility in the original, wide sense is acknowledged only if replication is performed by an independent researcher or team and it is successful.

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Introducing the World's First AI-Enabled Connected Classroom Technology
World's First AI-Enabled Connected Classroom Technology