Validating technique in psycohology

Accumulating evidence is the scientific community's method of self-correction.

Cross-validation avoids this risk by evaluating the performance of the model on an independent dataset (testing set). PRo NTo: pattern recognition for neuroimaging toolbox. doi: 10.1007/s12021-013-9178-1 Pub Med Abstract | Cross Ref Full Text | Google Scholar Simonsohn, U. Small telescopes: detectability and the evaluation of replication results. Accordingly, multiple propositions of scope, functioning and typology have been identified (Lykken, 1968; Schmidt, 2009; Hüffmeier et al., 2016; Schmidt and Oh, 2016; Le Bel et al., 2017). No use, distribution or reproduction is permitted which does not comply with these terms.A basic distinction is between direct replication and conceptual replication attempts (Zwaan et al., 2017). This protects against overfitting the training data. At the same time, it increases the confidence that the effects obtained in a specific study will be replicated, instantiating a simulated replication of the original study.

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Next, we describe different cross-validation schemes (section Cross-Validation Schemes), and finally, we conclude by highlighting some of the platforms for implementing cross-validation (section How to Apply Cross-Validation) and certain limitations (section Conclusions). This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).

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