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Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method.For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated.
Example of direct replication and conceptual replication There are two main types of replication in statistics. First, there is a type called “exact replication” (also called "direct replication"), which involves repeating the study as closely as possible to the original to see whether the original results can be precisely reproduced. [ 3 ]
Goodman, Fanelli and Ioannidis define method reproducibility as "the provision of enough detail about study procedures and data so the same procedures could, in theory or in actuality, be exactly repeated." [2] This acception is largely synonymous with replicability in a computational context or reproducibility in an experimental context. In ...
Reproducibility in the narrow sense refers to re-examining and validating the analysis of a given set of data. Replication refers to repeating the experiment or study to obtain new, independent data with the goal of reaching the same or similar conclusions.
There are different reasons for performing a round-robin test: determination the reproducibility of a test method or process; verification of a new method of analysis. If a new method of analysis has been developed, a round-robin test involving proven methods would verify whether the new method produces results that agree with the established method.
The Reproducibility Project is a series of crowdsourced collaborations aiming to reproduce published scientific studies, finding high rates of results which could not be replicated. It has resulted in two major initiatives focusing on the fields of psychology [ 1 ] and cancer biology. [ 2 ]
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ANOVA gauge repeatability and reproducibility is a measurement systems analysis technique that uses an analysis of variance (ANOVA) random effects model to assess a measurement system. The evaluation of a measurement system is not limited to gauge but to all types of measuring instruments , test methods , and other measurement systems.