  Feedforward neural networks (FNNs) are typically viewed as pure prediction
algorithms, and their strong predictive performance has led to their use in
many machine-learning applications. However, their flexibility comes with an
interpretability trade-off; thus, FNNs have been historically less popular
among statisticians. Nevertheless, classical statistical theory, such as
significance testing and uncertainty quantification, is still relevant.
Supplementing FNNs with methods of statistical inference, and covariate-effect
visualisations, can shift the focus away from black-box prediction and make
FNNs more akin to traditional statistical models. This can allow for more
inferential analysis, and, hence, make FNNs more accessible within the
statistical-modelling context.
