Interpreting histograms. As easy as it seems?

Lem, Stephanie; Onghena, Patrick; Verschaffel, Lieven; Dooren, Wim Van

doi:10.1007/s10212-014-0213-x

Cited by 8 publications

(10 citation statements)

References 12 publications

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“…Statistics experts (e.g., graduate students, professors, researchers) seem to be susceptible to influences from the vertical dimension as well. This was suggested by worse performance (i.e., more errors, slower response times) consistent with using the height heuristic (i.e., histograms with taller bars have greater means) for comparing histograms with the same mean and shape of distribution, but with scaled up heights of bars, but not with other types of histogram pairs (Lem, Onghena, Verschaffel, & Van Dooren, 2014). Even with experts, we see some level of perceptual interference with statistical reasoning.…”

Section: Perceptual Barriers To Reading Histogramsmentioning

confidence: 71%

“…Even with experts, we see some level of perceptual interference with statistical reasoning. Lem et al (2014) found that experts had slower response times when height information could bias one to an incorrect response (incongruent trials) than when height information was sufficient for the correct response (congruent trials). This pattern of slower response times for incongruent trials is characteristic of the Stroop effect.…”

Section: Perceptual Barriers To Reading Histogramsmentioning

confidence: 93%

“…The Stroop effect has been found for incongruence between the magnitude of numbers and their font size in numerical magnitude comparison tasks (e.g., when comparing 3 with 5, slower to select 3 as being the smaller number if it is printed in larger font; Henik & Tzelgov, 1982). In Lem et al (2014), choosing the correct answer in the histogram comparisons may have required the suppression of irrelevant or distracting information and reasoning beyond the perceptually intuitive answer. However, that study also highlighted the importance of conceptual understandingthe undergraduates did not perform any better when they were given unlimited time to make the comparison and warned about potential fallacies in graphs.…”

Section: Perceptual Barriers To Reading Histogramsmentioning

confidence: 99%

See 2 more Smart Citations

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Cui

Liu

2021

Atten Percept Psychophys

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In the age of big data, we are constantly inventing new data visualizations to consolidate massive amounts of numerical information into smaller and more digestible visual formats. These data visualizations use various visual features to convey quantitative information, such as spatial position in scatter plots, color saturation in heat maps, and area in dot maps. These data visualizations are typically composed of ensembles, or groups of related objects, that together convey information about a data set. Ensemble perception, or one's ability to perceive summary statistics from an ensemble, such as the mean, has been used as a foundation for understanding and explaining the effectiveness of certain data visualizations. However, research in data visualization has revealed some perceptual biases and conceptual difficulties people face when trying to utilize the information in these graphs. In this tutorial review, we will provide a broad overview of research conducted in ensemble perception, discuss how principles of ensemble encoding have been applied to the research in data visualization, and showcase the barriers graphs can pose to learning statistical concepts, using histograms as a specific example. The goal of this tutorial review is to highlight possible connections between three areas of research-ensemble perception, data visualization, and statistics education-and to encourage research in the practical applications of ensemble perception in solving real-world problems in statistics education.

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Section: Perceptual Barriers To Reading Histogramsmentioning

confidence: 71%

Section: Perceptual Barriers To Reading Histogramsmentioning

confidence: 93%

Section: Perceptual Barriers To Reading Histogramsmentioning

confidence: 99%

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Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Cui

Liu

2021

Atten Percept Psychophys

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“…5, was not large. One explanation for our findings regarding the effect of level of statistical training could relate to interpretational difficulties on the part of the statistically-trained group (e.g., Allen, Dorozenko & Roberts, 2016; Lem et al, 2014a). For example, in some instances, statistically-trained participants may be more likely to focus upon the structural/analytic, as opposed to surface, components of figures (Lem et al, 2014b).…”

Section: Discussionmentioning

confidence: 87%

Exploring perceptions of meaningfulness in visual representations of bivariate relationships

Beribisky

Davidson

Cribbie

2019

PeerJ

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Researchers often need to consider the practical significance of a relationship. For example, interpreting the magnitude of an effect size or establishing bounds in equivalence testing requires knowledge of the meaningfulness of a relationship. However, there has been little research exploring the degree of relationship among variables (e.g., correlation, mean difference) necessary for an association to be interpreted as meaningful or practically significant. In this study, we presented statistically trained and untrained participants with a collection of figures that displayed varying degrees of mean difference between groups or correlations among variables and participants indicated whether or not each relationship was meaningful. The results suggest that statistically trained and untrained participants differ in their qualification of a meaningful relationship, and that there is significant variability in how large a relationship must be before it is labeled meaningful. The results also shed some light on what degree of relationship is considered meaningful by individuals in a context-free setting.

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“…A substantial amount of literature has been devoted to the ability of students to correctly read and interpret histograms. In fact, various studies have been run to examine this, including those by Batanero et al (2005), Chaput et al (2021), delMas et al (2005, Kaplan et al (2014), and Lem et al (2013Lem et al ( , 2014. The studies found that students (and, in one case, some faculty) possess misconceptions about histograms.…”

Section: Introductionmentioning

confidence: 99%

NormalityAssessment: An Interactive Classroom Tool for Testing Normality Visually

Casement¹,

McSweeney²

2022

Technology Innovations in Statistics Education

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Many inferential and predictive statistical procedures possess underlying theoretical conditions that should be met in order for the results of those procedures to be considered reliable. One condition associated with methods for population means, including linear regression coefficients, is that of normality of a population(s). When assessing normality, two graphical tools that are often utilized are normal quantile-quantile (QQ) plots and histograms. However, while these tools are popular, they still present challenges for many who use them due to the subjectivity oftentimes involved when examining them. In this article, we describe a free, interactive Shiny application, downloadable as an R package, which implements two procedures recently developed for graphical inference with a specific emphasis on assessing normality. As the application was created and designed with a focus on statistics education, we also discuss a student assessment of it and, in the appendix, provide a suggested classroom activity.

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Interpreting histograms. As easy as it seems?

Cited by 8 publications

References 12 publications

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Exploring perceptions of meaningfulness in visual representations of bivariate relationships

NormalityAssessment: An Interactive Classroom Tool for Testing Normality Visually

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