  The main goal of this thesis is to point out that the bias-variance tradeoff
is not always true (e.g. in neural networks). We advocate for this lack of
universality to be acknowledged in textbooks and taught in introductory courses
that cover the tradeoff. We first review the history of the bias-variance
tradeoff, its prevalence in textbooks, and some of the main claims made about
the bias-variance tradeoff. Through extensive experiments and analysis, we show
a lack of a bias-variance tradeoff in neural networks when increasing network
width. Our findings seem to contradict the claims of the landmark work by Geman
et al. (1992). Motivated by this contradiction, we revisit the experimental
measurements in Geman et al. (1992). We discuss that there was never strong
evidence for a tradeoff in neural networks when varying the number of
parameters. We observe a similar phenomenon beyond supervised learning, with a
set of deep reinforcement learning experiments. We argue that textbook and
lecture revisions are in order to convey this nuanced modern understanding of
the bias-variance tradeoff.
