  Understanding and comparing distributions of data (e.g., regarding their
modes, shapes, or outliers) is a common challenge in many scientific
disciplines. Typically, this challenge is addressed using side-by-side
comparisons of histograms or density plots. However, comparing multiple density
plots is mentally demanding. Uniform histograms often represent distributions
imprecisely since missing values, outliers, or modes are hidden by a grouping
of equal size. In this paper, a novel type of overview visualization for the
comparison of univariate data distributions is presented: AccuStripes (i.e.,
accumulated stripes) is a new visual metaphor encoding accumulations of data
distributions according to adaptive binning using color coded stripes of
irregular width. We provide detailed insights about challenges of binning.
Specifically, we explore different adaptive binning concepts such as Bayesian
Blocks binning and Jenks Natural Breaks binning for the computation of binning
boundaries, in terms of their capabilities to represent the datasets as
accurately as possible. In addition, we discuss issues arising with the
representation of designs for the comparative visualization of distributions:
To allow for a comparison of many distributions, their accumulated
representations are plotted below each other in a stacked mode. Based on our
findings, we propose three different layouts for comparative visualization of
multiple distributions. The usefulness of AccuStripes is investigated using a
statistical evaluation of the binning methods. Using a similarity metric from
cluster analysis, it is shown, which binning method statistically yields the
best grouping results. Through a user study we evaluate, which binning strategy
visually represents the distribution in the most intuitive form and
investigate, which layout allows the user the comparison of many distributions
in the most effortless way.
