Reasoning Affordances With Tables and Bar Charts.

A viewer's existing beliefs can prevent accurate reasoning with data visualizations. In particular, confirmation bias can cause people to overweigh information that confirms their beliefs, and dismiss information that disconfirms them. We tested whether confirmation bias exists when people reason with visualized data and whether certain visualization designs can elicit less biased reasoning strategies.

From Invisible to Visible: Impacts of Metadata in Communicative Data Visualization.

Leaving the context of visualizations invisible can have negative impacts on understanding and transparency. While common wisdom suggests that recontextualizing visualizations with metadata (e.g.

Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes.

Visualization research often focuses on perceptual accuracy or helping readers interpret key messages. However, we know very little about how chart designs might influence readers' perceptions of the people behind the data. Specifically, could designs interact with readers' social cognitive biases in ways that perpetuate harmful stereotypes? For example, when analyzing social inequality, bar charts are a popular choice to present outcome disparities between race, gender, or other groups.

Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation.

When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. Therefore, one bedrock principle of science is to minimize that bias by testing the predictions of one's belief against objective data. But interpreting visualized data is a complex perceptual and cognitive process.

How Do Viewers Synthesize Conflicting Information from Data Visualizations?

Scientific knowledge develops through cumulative discoveries that build on, contradict, contextualize, or correct prior findings. Scientists and journalists often communicate these incremental findings to lay people through visualizations and text (e.g.

Comparison Conundrum and the Chamber of Visualizations: An Exploration of How Language Influences Visual Design.

The language for expressing comparisons is often complex and nuanced, making supporting natural language-based visual comparison a non-trivial task. To better understand how people reason about comparisons in natural language, we explore a design space of utterances for comparing data entities. We identified different parameters of comparison utterances that indicate what is being compared (i.

VisHikers' Guide to Evaluation: Competing Considerations in Study Design.

In this Viewpoint article, we describe the persistent tensions between various camps on the "right" way to conduct evaluations in visualization. Visualization as a field is the amalgamation of cognitive and perceptual sciences and computer graphics, among others. As a result, the relatively disjointed lineages in visualization understandably approach the topic of evaluation very differently.

Explaining With Examples: Lessons Learned From Crowdsourced Introductory Description of Information Visualizations.

Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. Clear verbal introductions of visualizations to explain how to interpret the visually encoded information are essential to convey the takeaways and avoid misunderstandings. We contribute a series of studies to investigate how to effectively introduce visualizations to the audience with varying degrees of visualization literacy.

Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways.

Well-designed data visualizations can lead to more powerful and intuitive processing by a viewer. To help a viewer intuitively compare values to quickly generate key takeaways, visualization designers can manipulate how data values are arranged in a chart to afford particular comparisons. Using simple bar charts as a case study, we empirically tested the comparison affordances of four common arrangements: vertically juxtaposed, horizontally juxtaposed, overlaid, and stacked.

Designing With Pictographs: Envision Topics Without Sacrificing Understanding.

Past studies have shown that when a visualization uses pictographs to encode data, they have a positive effect on memory, engagement, and assessment of risk. However, little is known about how pictographs affect one's ability to understand a visualization, beyond memory for values and trends. We conducted two crowdsourced experiments to compare the effectiveness of using pictographs when showing part-to-whole relationships.

Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication.

Data visualization design has a powerful effect on which patterns we see as salient and how quickly we see them. The visualization practitioner community prescribes two popular guidelines for creating clear and efficient visualizations: declutter and focus. The declutter guidelines suggest removing non-critical gridlines, excessive labeling of data values, and color variability to improve aesthetics and to maximize the emphasis on the data relative to the design itself.

Antibiotic Resistance of Isolated from , , and Retail Broiler Chicken Meat.

The use of antibiotics for therapeutic and especially non-therapeutic purposes in livestock farms promotes the development of antibiotic resistance in previously susceptible bacteria through selective pressure. In this work, we examined isolates using the standard Kirby-Bauer disk diffusion susceptibility protocol and the CLSI standards. Companies selling retail chicken products in Los Angeles, California were grouped into three production groupings-, , and .

A Design Space of Vision Science Methods for Visualization Research.

A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to more effective visualization practice. However, published visualization research currently reflects a limited set of available methods for understanding how people process visualized data.

Truth or Square: Aspect Ratio Biases Recall of Position Encodings.

Bar charts are among the most frequently used visualizations, in part because their position encoding leads them to convey data values precisely. Yet reproductions of single bars or groups of bars within a graph can be biased. Curiously, some previous work found that this bias resulted in an overestimation of reproduced data values, while other work found an underestimation.

Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull.

In visual depictions of data, position (i.e., the vertical height of a line or a bar) is believed to be the most precise way to encode information compared to other encodings (e.

Illusion of Causality in Visualized Data.

Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program would lead to higher grades. This is a reasoning error, because correlation does not necessarily indicate causation - X and Y can be correlated without one directly causing the other.

The Curse of Knowledge in Visual Data Communication.

A viewer can extract many potential patterns from any set of visualized data values. But that means that two people can see different patterns in the same visualization, potentially leading to miscommunication. Here, we show that when people are primed to see one pattern in the data as visually salient, they believe that naïve viewers will experience the same visual salience.