Data Visualization: The Signal and the Noise

Graphics are often mistaken for a mere frill in the methodological arsenal of data analysis when in fact they can be one of the simplest and at the same time most powerful methods of communicating statistical information (Tufte, 2001). The first section of the article argues for the statistical necessity of graphs, echoing and amplifying similar calls from Hudson (2015) and Larson–Hall & Plonsky (2015). The second section presents a historical survey of graphical use over the entire history of language acquisition journals, spanning a total of 192 years. This shows that a consensus for using certain types of graphics, which lack data credibility, has developed in the applied linguistics field, namely the bar plot and the line graph. The final section of the article is devoted to presenting various types of graphic alternatives to these two consensus graphics. Suggested graphics are data accountable and present all of the data, as well as a summary structure; such graphics include the scatterplot, beeswarm or pirate plot. It is argued that the use of such graphics attracts readers, helps researchers improve the way they understand and analyze their data, and builds credibility in numerical statistical analyses and conclusions that are drawn.

2004

Wider range of graphs, tailored to analysis Some need/want control of graphic styles, rendering details Graphics developer Freedom to invent new methods of visualization with ease Elegant connections between statistical analysis (summarization) and visualization (exposure)

Numeracy, 2019

Claus O. Wilke has authored an excellent reference about producing and understanding static figures, figures used online, in print, and for presentations. His book is neither a statistics nor programming text, but familiarity with basic statistical concepts is helpful. Written in three parts, the book presents both the math and artistic design aspects of telling a story through figures. Wilke makes extensive use of examples, labels them good, bad, wrong or ugly, and explains why he deems them so. He includes chapters that serve as a directory of visualizations, and the reader will encounter familiar figure types as well as more esoteric ones. He discusses aesthetic considerations, including color, that make figures more compelling, and provides tips to connect with the audience to tell a story. Wilke intends the book for those producing figures, but consumers of graphical content will also find it helpful as his clear and concise style-and his effective use of exampleswill help them evaluate the worth of figures they encounter.

2020

Statisticians recommend graphical displays but often use tables to present their own research results. Could graphs do better? We study the question by going through the tables in a recent issue of the Journal of the American Statistical Association. We show how it is possible to improve the presentationsusing graphs that actually take up less space than the original tables. We nd a particularly effective tool to be multiple repeated line plots, with comparisons of interest connected by lines and separate comparisons isolated on different plots.

Graphics are good for showing the information in datasets and for complementing modelling. Sometimes graphics show information models miss, sometimes graphics help to make model results more understandable, and sometimes models show whether information from graphics has statistical support or not. It is the interplay of the two approaches that is valuable. Graphics could be used a lot more in R examples and we explore this idea with some datasets available in R packages.

The American Journal of Gastroenterology, 2009

In addition to being more memorable than tables, graphics can also illustrate multiple dimensions and important relationships far better than tables. Edward Tufte says it best (6): "Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numberseven a very large set-is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful. " In this paper I highlight some of the best plots published in AJG in the past year, show some graphics that could be improved, and illustrate how perhaps one plot could convey information more directly than a table.

2002

Statisticians recommend graphical displays but often use tables to present their own research results. Could graphs do better? We study the question by going through the tables in a recent issue of the Journal of the American Statistical Association. We show how it is possible to improve the presentationsusing graphs that actually take up less space than the original tables. We nd a particularly effective tool to be multiple repeated line plots, with comparisons of interest connected by lines and separate comparisons isolated on different plots.

Journal of the history of the behavioral sciences, 2005

Of all the graphic forms used today, the scatterplot is arguably the most versatile, polymorphic, and generally useful invention in the history of statistical graphics. Its use by Galton led to the discovery of correlation and regression, and ultimately to much of present multivariate statistics. So, it is perhaps surprising that there is no one widely credited with the invention of this idea. Even more surprising is that there are few contenders for this title, and this question seems not to have been raised before. This article traces some of the developments in the history of this graphical method, the origin of the term scatterplot, the role it has played in the history of science, and some of its modern descendants. We suggest that the origin of this method can be traced to its unique advantage: the possibility to discover regularity in empirical data by smoothing and other graphic annotations to enhance visual perception.

ods. Where two features in a study can be measured accurately, a visual presentation such as a scatter plot may indicate an interesting relationship, if it does not seem random. This helps researchers to understand the relation between different variables in a particular dataset. It also aids investigators to make the appropriate decision about how to further analyze the data by applying the most suitable statistical models. The chief aim of the present article therefore, is to examine one of the most powerful graphical diagrams for data visualization i.e. a scatter plot using a real public health dataset.

The American Statistician, 2015

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into 'pictures', and develop interactive visualizations with, e.g., the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, e.g., Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education.