cross-validation of findings from a prediction study is particularly important for
Cross-validation (statistics) Cross-validation, sometimes called rotation estimation,[1][2][3] or out-of-sample testing is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (called the validation dataset or testing set).[4],[5] The goal of cross-validation is to test the modelâ€™s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias[6] and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).
Scientific theory A scientific theory is an explanation of an aspect of the natural world that can be repeatedly tested, in accordance with the scientific method, using a predefined protocol of observation and experiment.[1][2] Established scientific theories have withstood rigorous scrutiny and embody scientific knowledge.[3]