Different error types may be caused by a single source. In our treatment of the bias and variance error present in the noiseless dataset, we note that bias and variance errors both increase in circumstances with small dataset sizes and small model sizes. A single source, whether it is data sparsity or a problem of model architecture choice, can manifest simultaneously as both kinds of errors. Similarly, our experiments with the addition of controlled noise to a dataset showed separate reducible and irreducible errors depending on whether the noise was in the test set or used in training, an example of noise addition leading to both noise and bias errors. Interactions, trends, and correlations between error types will exist in real datasets that go beyond simple error type assignment.