  Standard datasets are frequently used to train and evaluate Machine Learning
models. However, the assumed standardness of these datasets leads to a lack of
in-depth discussion on how their labels match the derived categories for the
respective use case. In other words, the standardness of the datasets seems to
fog coherency and applicability, thus impeding the trust in Machine Learning
models. We propose to adopt Grounded Theory and Hypotheses Testing through
Visualization as methods to evaluate the match between use case, derived
categories, and labels of standard datasets. To showcase the approach, we apply
it to the 20 Newsgroups dataset and the MNIST dataset. For the 20 Newsgroups
dataset, we demonstrate that the labels are imprecise. Therefore, we argue that
neither a Machine Learning model can learn a meaningful abstraction of derived
categories nor one can draw conclusions from achieving high accuracy. For the
MNIST dataset, we demonstrate how the labels can be confirmed to be defined
well. We conclude that a concept of standardness of a dataset implies that
there is a match between use case, derived categories, and class labels, as in
the case of the MNIST dataset. We argue that this is necessary to learn a
meaningful abstraction and, thus, improve trust in the Machine Learning model.
