  In recent years, progress in the Visual Question Answering (VQA) field has
largely been driven by public challenges and large datasets. One of the most
widely-used of these is the VQA 2.0 dataset, consisting of polar ("yes/no") and
non-polar questions. Looking at the question distribution over all answers, we
find that the answers "yes" and "no" account for 38 % of the questions, while
the remaining 62% are spread over the more than 3000 remaining answers. While
several sources of biases have already been investigated in the field, the
effects of such an over-representation of polar vs. non-polar questions remain
unclear. In this paper, we measure the potential confounding factors when polar
and non-polar samples are used jointly to train a baseline VQA classifier, and
compare it to an upper bound where the over-representation of polar questions
is excluded from the training. Further, we perform cross-over experiments to
analyze how well the feature spaces align. Contrary to expectations, we find no
evidence of counterproductive effects in the joint training of unbalanced
classes. In fact, by exploring the intermediate feature space of visual-text
embeddings, we find that the feature space of polar questions already encodes
sufficient structure to answer many non-polar questions. Our results indicate
that the polar (P) and the non-polar (NP) feature spaces are strongly aligned,
hence the expression P $\approx$ NP
