Scaling up psychology via Scientific Regret Minimization.

ABSTRACT: Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets. One traditional approach is to analyze the residuals of models-the biggest errors they make in predicting the data-to discover what might be missing from those models. However, once a dataset is sufficiently large, machine learning algorithms approximate the true underlying function better than the data, suggesting, instead, that the predictions of these data-driven models should be used to guide model building. We call this approach "Scientific Regret Min

SUBMITTER: Agrawal M 

PROVIDER: S-EPMC7183163 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature