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Understanding and analyzing univariate distributions of data in terms of their shapes as well as their specific characteristics, regarding gaps, spikes, or outliers, is crucial in many scientific disciplines. In this paper, we propose a design space composed of the visual channels position and color for representing accumulated distributions. The designs are a mixture of color-coded stripes with density lines. The width and coloring of the stripes is based on the applied binning technique. In a crowd-sourced experiment we explore a subspace, called the AccuStripes (i.e., "accumulated stripes") design space, consisting of nine representations. These AccuStripes designs integrate three composition strategies (color only, overlay, filled curve) with three binning techniques, one uniform (UB) and two adaptive methods, namely Bayesian Blocks (BB) and Jenks' Natural Breaks (NB). We evaluate the accuracy, efficiency, and confidence ratings of the nine AccuStripes designs for structural estimation and comparison tasks. Across all study tasks, the overlay composition was found to be most accurate and preferred by observers. Furthermore, the results demonstrate that while no binning method performed best in both identification and comparison, detection of structures using adaptive binning was the most accurate one. For validation we compared the best AccuStripes' design, i.e., the overlay composition, to line charts. Our results show that the AccuStripes' design outperformed the line charts in accuracy for all study tasks. [Display omitted] • Novel design space to represent and compare univariate data distributions. • A subspace is introduced combining color-coded stripes with density lines. • The stripes' width and coloring are defined by adaptive and uniform binning methods. • Evaluation and validation of the subspace through two crowdsource studies. [ABSTRACT FROM AUTHOR]