# Classical Statistics and Statistical Learning in Imaging Neuroscience

arXiv: https://arxiv.org/abs/1603.01857 · HTML: https://mlsanity.com/paper/1603.01857/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2016.

## Built on by (most cited first, 3 of 3)

- [Artificial Intelligence in Healthcare: Lost In Translation?](https://mlsanity.com/paper/2107.13454/) (2021)
- [A hypothesis-driven method based on machine learning for neuroimaging data analysis](https://mlsanity.com/paper/2202.04397/) (2022)
- [Machine learning applications in archaeological practices: a review](https://mlsanity.com/paper/2501.03840/) (2025)

## Abstract (arXiv)

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.