Letters to the Editor

Letters to the editor will be confined to discussions of papers that have appeared in The American Statistician and to important issues facing the statistical community. Letters discussing papers in The American Statistician must be received within two months of publication of the paper; the author of the paper will then be given an opportunity to reply, and the letters and reply will be published together. All letters to the editor will be refereed. Corrections of errors that have been noted in papers published in The American Statistician will be listed as corrections at the end of this section.     

Letters to the Editor

Letters to the editor will be confined to discussions of papers that have appeared in The American Statistician and of important issues facing the statistical community. Letters discussing papers in The American Statistician must be received within two months of publication of the paper; the author of the paper will then be given an opportunity to reply, and the letters and reply will be published together. All letters to the editor will be refereed. Corrections of errors that have been noted in papers published in The American Statistician will be listed as corrections at the end of this section.     

Letters to the Editor

Quantitative graphics have been central to the development of science, and statistical graphics date from the earliest attempts to analyze data. Many familiar forms, including bivariate plots, statistical maps, bar charts, and coordinate paper, were used in the 18th century. Statistical graphics developed through attention to four problems: spatial organization (17th and 18th centuries), discrete comparison (18th and early 19th centuries), continuous distribution (19th century), and multivariate distribution and correlation (late 19th and early 20th centuries). Today, statistical graphics appear to be reemerging as an important analytic tool, with recent innovations exploiting computer graphics and related technologies.     

Letters to the Editor

Although several authors have indicated that the median test has low power in small samples, it continues to be presented in many statistical textbooks, included in a number of popular statistical software packages, and used in a variety of application areas. We present results of a power simulation study that shows that the median test has noticeably lower power, even for the double exponential distribution for which it is asymptotically most powerful, than other readily available rank tests. We suggest that the median test be “retired” from routine use and recommend alternative rank tests that have superior power over a relatively large family of symmetric distributions.