Wolford, and two anonymous reviewers for very useful comments on earlier drafts of the manuscript. Correspondence may be addressed to

After 4 decades of severe criticism, the ritual of null hypothesis significance testing—mechanical dichotomous decisions around a sacred.05 criterion—still persists. This article reviews the problems with this practice, including its near-universal misinterpretation ofp as the probability that Ho is false, the misinterpretation that its complement is the probability of successful replication, and the mistaken assumption that if one rejects Ho one thereby affirms the theory that led to the test. Exploratory data analysis and the use of graphic methods, a steady improvement in and a movement toward standardization in measurement, an emphasis on estimating effect sizes using confidence intervals, and the informed use of available statistical methods is suggested. For generalization, psychologists must finally rely, as has been done in all the older sciences,

Abstract—We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods. Index Terms—User study, vector visualization, fluid flow visualization. 1

Excel is the most widespread and the most powerful general-purpose spreadsheet software, but it is not popular with statisticians. Nevertheless, as a natural means for organising, displaying and analysing large amounts of data, spreadsheets keep gaining importance in statistical education and practice. Aiming at improving such practice rather than fruitlessly and indiscriminately condemning it, the paper provides general considerations on the topic, pointers to the huge body of relevant literature and software, and several concrete examples of data visualisation in Excel in the sense of univariate, bivariate and multivariate distribution plotting. Original and improved Excel solutions for producing dot-density plots, dot plots, stem-and-leaf plots, windowgrams, coplots and parallel coordinates plots are presented, as well as for performing the Box-Cox transformation. Additionally, further possibilities opening with the forthcoming Excel 2007 version, use of various commercial and freeware add-ins, and integration of Excel with statistical software are discussed. 1

Historically, data visualization has been limited primarily to 2 dimensions (e.g., histograms, scatter plots). Available software packages (e.g., Data Desk 4.04, SPSS 10.0) are capable of producing 3-D scatter plots with (varying degrees of) user interactivity. We constructed our own data visualization application with The Visualization Toolkit (Schroeder, Martin, & Lorensen, 1998) and Tcl/Tk to display multivariate data through the application of glyphs (Ware, 2000). A glyph is a visual object onto which many data parameters may be mapped, each with a different visual attribute (e.g., size, color). We used our Multi-Dimensional Data Viewer to explore data from several psycholinguistic experiments. The graphical interface provides flexibility when users dynamically explore the multi-dimensional image rendered from raw experimental data. We highlight advantages of multidimensional data visualization and consider some potential limitations.

Abstract not found