Comparing Overlapping Data Distributions Using Visualization

Eric Newburger; Niklas Elmqvist

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Comparing Overlapping Data Distributions Using Visualization

Eric Newburger, Niklas Elmqvist

Human-Computer Interaction · Submitted 22 Sep 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim However, our results indicate that the more abstracted idealized bell curve representation of the task yields more accuracy.

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,972 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.48 · human participants 0.98 · controlled comparison 0.52 · randomized assignment 0.07 · states quantitative result 0.07 · compares with prior results 0.76 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

We present results from a preregistered and crowdsourced user study where we asked members of the general population to determine whether two samples represented using different forms of data visualizations are drawn from the same or different populations. Such a task reduces to assessing whether the overlap between the two visualized samples is large enough to suggest similar or different origins. When using idealized normal curves fitted on the samples, it is essentially a graphical formulation of the classic Student's t-test. However, we speculate that using more sophisticated visual representations, such as bar histograms, Wilkinson dot plots, strip plots, or Tukey boxplots will both allow people to be more accurate at this task as well as better understand its meaning. In other words, the purpose of our study is to explore which visualization best scaffolds novices in making graphical inferences about data. However, our results indicate that the more abstracted idealized bell curve representation of the task yields more accuracy.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	We present results from a preregistered and crowdsourced user study where we asked members of the general population to determine whether two samples represented using different forms of data visualizations are drawn from the same or different populations.	contribution 0.82	0.95	0.03	asserted 0.97
s1	Such a task reduces to assessing whether the overlap between the two visualized samples is large enough to suggest similar or different origins.	background 0.83	0.11	0.03	asserted 0.97
s2	When using idealized normal curves fitted on the samples, it is essentially a graphical formulation of the classic Student's t-test.	background 0.80	0.12	0.03	asserted 0.97
s3	However, we speculate that using more sophisticated visual representations, such as bar histograms, Wilkinson dot plots, strip plots, or Tukey boxplots will both allow people to be more accurate at this task as well as better understand its meaning.	gap 0.46	0.06	0.03	speculative 1.00
s4	In other words, the purpose of our study is to explore which visualization best scaffolds novices in making graphical inferences about data.	contribution 0.95	0.19	0.04	asserted 0.97
s5	However, our results indicate that the more abstracted idealized bell curve representation of the task yields more accuracy.	result 1.00	0.94	0.88	hedged possibility 0.61

What kind of paper the classifier reports this is: empirical study 1.00 · evidence from observation or experiment 1.00.

What was examined, and what was not

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arXiv listing abstract v1

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full text
figures and tables
references and citing sentences
relations to other papers

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P abstract

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

We present results from a preregistered and crowdsourced user study where we asked members of the general population to determine whether two samples represented using different forms of data visualizations are drawn from the same or different populations. Such a task reduces to assessing whether the overlap between the two visualized samples is large enough to suggest similar or different origins. When using idealized normal curves fitted on the samples, it is essentially a graphical formulation of the classic Student's t-test. However, we speculate that using more sophisticated visual representations, such as bar histograms, Wilkinson dot plots, strip plots, or Tukey boxplots will both allow people to be more accurate at this task as well as better understand its meaning. In other words, the purpose of our study is to explore which visualization best scaffolds novices in making graphical inferences about data. However, our results indicate that the more abstracted idealized bell curve representation of the task yields more accuracy.

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