The separation of the total uncertainty into aleatoric (data-dependent, noise-induced, irreducible) and epis-temic (model-dependent, reducible) contributions has recently received increasing attention. The aleatoric uncertainty is often referred to as the irreducible component of uncertainty that cannot be overcome by improvements to the model. Reduction in aleatoric uncertainty can instead come from improvements in the data itself, such as adding repeat measurements or removing erroneous entries. In contrast, epistemic uncertainty characterizes the reducible uncertainty caused by missing knowledge and can be decreased as the model is improved. The epistemic uncertainty can further be split into uncertainty arising from the choice of model (architecture, representation, and featurization) and the ambiguity of parameter optimization once a model is chosen. In this work, we follow the convention of calling the former model bias and the latter variance, but different other names are sometimes used in the literature, such as model uncertainty and approximation uncertainty. The difference between reducible and irreducible uncertainty can become blurred in these considerations, especially for different model architectures, different representations, and different data and test sets. Small dataset sizes contribute to both bias and variance components of epistemic uncertainty because they cause some ambiguity in the optimal model parameters due to sparsity in some regions, but also hinder the model convergence to a meaningful minimum generally. The size or nature of the data may additionally influence the choice of model architecture or machine learning method, providing a further avenue by which aspects of the data can feed into epistemic uncertainty.