Classical Statistics and Statistical Learning in Imaging Neuroscience
This work addresses methodological confusion for neuroimaging researchers, but it is incremental as it synthesizes existing knowledge without introducing new techniques.
The paper examines the differences and commonalities between classical statistics and statistical learning methods in neuroimaging, illustrating their conceptual implications in three analysis scenarios to clarify confusion between hypothesis testing and model estimation.
Neuroimaging research has predominantly drawn conclusions based on classical statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity, including cross-validation, pattern classification, and sparsity-inducing regression. These two methodological families used for neuroimaging data analysis can be viewed as two extremes of a continuum. Yet, they originated from different historical contexts, build on different theories, rest on different assumptions, evaluate different outcome metrics, and permit different conclusions. This paper portrays commonalities and differences between classical statistics and statistical learning with their relation to neuroimaging research. The conceptual implications are illustrated in three common analysis scenarios. It is thus tried to resolve possible confusion between classical hypothesis testing and data-guided model estimation by discussing their ramifications for the neuroimaging access to neurobiology.