What is cross-validation in research?

Cross-validation is a statistical method used to assess how the results of a statistical analysis will generalize to an independent dataset. By splitting the available data into subsets, researchers can train their model on one subset and test it on another. This process helps in verifying that the model performs consistently and robustly across varied data. Essentially, it mitigates the risk of overfitting, where a model might perform well on training data but poorly on unseen data. By validating the model’s performance on separate portions of the dataset, researchers can have greater confidence in the model's predictive abilities and its applicability to new data, which is a crucial aspect of research methodology in fields like clinical psychology.