Search

Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently stressed in a wide range of scientific journals such as Nature and Science and international magazines such as The Economist. However, multiple studies have demonstrated that scientific results are often not reproducible across research areas such as psychology and medicine. Statistics, the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data, prides itself on its openness when it comes to sharing both computer code and data. In this article, we examine reproducibility in the field of statistics by attempting to reproduce the results in 93 published papers in prominent journals using functional magnetic resonance imaging (fMRI) data during the 2010–2021 period. Overall, from both the computer code and the data perspective, among all the 93 examined papers, we could only reproduce the results in 14 (15.1%) papers, that is, the papers provide both executable computer code (or software) with the real fMRI data, and our results matched the results in the paper. Finally, we conclude with some author-specific and journal-specific recommendations to improve the research reproducibility in statistics. [ABSTRACT FROM AUTHOR]

This article reviews Albert Einstein's first published paper, submitted for publication in 1900. At that time, Einstein was 21 and a recent college graduate. His paper uses modeling and least squares to analyze data in support of a scientific proposition. Einstein is shown to be well trained, for his day, in using statistics as a tool in his scientific research. This paper also shows his ability to make trivial arithmetic mistakes and some clumsiness in data recording. A major aim of this article is to help provide a better appreciation of Einstein as an active user of statistical arguments in this and other of his important publications. [ABSTRACT FROM AUTHOR]

Dissemination of ideas from theory to practice is a significant challenge in statistics. Quick identification of articles useful to practitioners would greatly assist in this dissemination, thereby improving science. This article uses the citation count history of articles to identify key papers from 1985 to 2002 from 12 statistics journals for applied biostatisticians. One feature requiring attention in order to appropriately rank an article's impact is assessment of the citation accrual patterns over time. Citation counts in statistics differ dramatically from fields such as medicine. In statistics, most articles receive few citations, with 15-year-old articles from five key journals receiving a median of 13 citations compared to 66 in the Journal of Clinical Oncology. However, statistics articles in the top 2%-3% continue to gain citations at a high rate past 15 years, exceeding those in JCO, whose counts slow dramatically around 8 years past publication. Articles with the highest expected applied uses 20 years post publication were identified using joinpoint regression. In this evaluation, the fraction of citations that represent applied use was defined and estimated. The false discovery rate, quantification of heterogeneity in meta-analysis, and generalized estimating equations rank as the ideas with the greatest estimated applied impact. [ABSTRACT FROM AUTHOR]

Presents rules and suggestions on technical writing related to statistics. Popular beliefs about technical writing; Discussion of five rules; Guidelines on arranging thoughts and ideas on paper.

The article discusses various reports published within the issue, including one on the problem of using batting average to estimate a baseball player's chance of getting a hit and the utilization of algorithm in residual plots.

The following three articles are the results of papers presented and discussed at a symposium entitled "Improving the Workforce of the Future: Opportunities in Undergraduate Education," held August 12-13, 2000, in Indianapolis, IN. This symposium was sponsored by the American Statistical Association through its Undergraduate Statistics Education Initiative. The first article was the keynote address at the symposium. The latter two articles are position papers that were developed in part through discussions among representatives from liberal arts colleges, research universities, industry, and government at a May 1999 meeting at the ASA Headquarters and a subsequent April 2000 workshop in Alexandria, VA. This workshop was partially supported by the National Science Foundation. Other position papers based on symposium discussions are being developed and are scheduled to appear in the Journal of Statistical Education. [ABSTRACT FROM AUTHOR]

Industrial statistics has a rich and proud heritage. The field was initiated in the 1920s and picked up steam in the 1950s with the establishment of industrial statistics groups in several companies including American Cyanamid, DuPont, General Electric, Kodak, Western Electric, Procter and Gamble, General Foods, and 3M. It can be argued that we are in the third generation of the development of the profession. Indeed we are standing on the shoulders of giants. Several pioneering industrial statistics organizations are profiled in this article. The focus is on the roots of the organizations, the people involved and their contributions to their employers, advancements in the field and the development of the profession. Synthesis of this information provides some unique insights into who we are, what we have accomplished, and the needs and opportunities of the future. [ABSTRACT FROM AUTHOR]

Ronald Fisher was probably the greatest statistician of the twentieth century. Gertrude Cox was an expert on experimental designs and an outstanding administrator and developer of statistical departments, first in North Carolina and then worldwide. These two associated in various roles for thirty years, on U.S. campuses and at international statistical associations. They were both founding members of the international Biometric Society in 1947. A major collision came in 1953, when Cox was editor of Biometrics. Fisher submitted an article critical of four contemporary statisticians and did not want to change it. Cox published his article and comments by the four, all in the same issue. There was deep underlying respect, however, and Fisher lectured and taught for Cox in North Carolina afterward. They continued to correspond, and to meet occasionally, until Fisher’s death in 1962. [ABSTRACT FROM AUTHOR]

The article presents information on the content and operation related to the periodical "The American Statistician," (TAS) for 2004. The Software Reviews Section presented seven reviews during the calendar year 2004. Each issue was accompanied with a Section Editor's Notes by Joseph Hilbe, which summarized the reviews in each issue, discussed other software, and relayed other related information of interest to the statistical community.

The article presents information on the content and operation related to the periodical "The American Statistician," (TAS) for 2004. In 2004, 237 books were received for review, and 46 of those were identified as teaching-related and hence designated for review in TAS. Full-length book reviews published in 2004 total 20. There were also two "brief reviews" articles in which 14 more books are reviewed, for a total of 34 books. This compares with 31 books reviewed in 2003, and 29 in 2002. Full reviews in 2004 include books on baseball, bio-statistics, common statistical errors, courseware, grade inflation, introductory statistics, practice problems, probability theory, proofs, R, regression and ANOVA, SAS, S-Plus, and stochastic modeling.

Scientific journals may counter the misuse, misreporting, and misinterpretation of statistics by providing guidance to authors. We described the nature and prevalence of statistical guidance at 15 journals (top-ranked by Impact Factor) in each of 22 scientific disciplines across five high-level domains (N = 330 journals). The frequency of statistical guidance varied across domains (Health & Life Sciences: 122/165 journals, 74%; Multidisciplinary: 9/15 journals, 60%; Social Sciences: 8/30 journals, 27%; Physical Sciences: 21/90 journals, 23%; Formal Sciences: 0/30 journals, 0%). In one discipline (Clinical Medicine), statistical guidance was provided by all examined journals and in two disciplines (Mathematics and Computer Science) no examined journals provided statistical guidance. Of the 160 journals providing statistical guidance, 93 had a dedicated statistics section in their author instructions. The most frequently mentioned topics were confidence intervals (90 journals) and p-values (88 journals). For six "hotly debated" topics (statistical significance, p-values, Bayesian statistics, effect sizes, confidence intervals, and sample size planning/justification) journals typically offered implicit or explicit endorsement and rarely provided opposition. The heterogeneity of statistical guidance provided by top-ranked journals within and between disciplines highlights a need for further research and debate about the role journals can play in improving statistical practice. [ABSTRACT FROM AUTHOR]

The article discusses various articles published within the issue, including one on statistician Samuel S. Wilks' contributions to statistics and another by W. Edwards Deming on Wilks award.

Galton's firstwork on regression probably led him to think of it as a unidirectional, genetic process, which he called "reversion."A subsequent experiment on family heightsmade him realize that the phenomenon was symmetric and nongenetic. Galton then abandoned "reversion" in favor of "regression." Final confirmation was provided through Dickson'smathematical analysis and Galton's examination of height data on brothers. [ABSTRACT FROM AUTHOR]

The article introduces previously published articles, related to statistics. The article by G.A. McIntyre was the first to introduce what has become known as rank set sampling. Published in the "Australian Journal of Agricultural Research," in 1952, the paper's initial application was to pasture measurement. A form of double sampling is being employed and, under fairly general conditions, gains can be made in sampling efficiency. In the August 2004 of the periodical "The American Statistician." there was an article by Gabor J. Székely and Donald St. P. Richards on an application of the St. Petersburg Paradox to the crash of high-tech stocks in 2000.

Describes a course in writing and critical appraisal of medical papers that uses statistics. Relationship of the course to a better integration of scientific writing into the statistics curriculum; Role of writing in statistical education; Model of scientific writing.

Presents letters on the corrections of errors noted on papers published in The American Statistician journal. Issues facing the statistical community; Estimation of probabilities and consequences of accidents; Prevention of abuses in the name of statistics.

The role of Wikipedia for learning has been debated because it does not conform to the usual standards. Despite this, people use it, due to the ubiquity of Wikipedia entries in the outcomes from popular search engines. It is important for academic disciplines, including statistics, to ensure they are correctly represented in a medium where anyone can assume the role of discipline expert. In this context, we first develop a tool for evaluating Wikipedia articles for topics with a procedural component. Then, using this tool, five Wikipedia articles on basic statistical concepts are critiqued from the point of view of a self-learner: "arithmetic mean," "standard deviation," "standard error," "confidence interval," and "histogram." We find that the articles, in general, are poor, and some articles contain inaccuracies. We propose that Wikipedia be actively discouraged for self-learning (using, for example, a classroom activity) except to give a brief overview; that in more formal learning environments, teachers be explicit about not using Wikipedia as a learning resource for course content; and, because Wikipedia is used regardless of considered advice or the organizational protocols in place, teachers move away from minimal contact with Wikipedia towards more constructive engagement. [ABSTRACT FROM AUTHOR]

The "New Statistics" emphasizes effect sizes, confidence intervals, meta-analysis, and the use of Open Science practices. We present three specific ways in which a New Statistics approach can help improve scientific practice: by reducing overconfidence in small samples, by reducing confirmation bias, and by fostering more cautious judgments of consistency. We illustrate these points through consideration of the literature on oxytocin and human trust, a research area that typifies some of the endemic problems that arise with poor statistical practice. [ABSTRACT FROM AUTHOR]

Primary credit for the introduction of matrix algebra into statistics is given to A.C. Aitken in the 1930s. [ABSTRACT FROM AUTHOR]

Misrepresented data and data taken out of context can be misleading at best. Statisticians present data to compel arguments, and they have a responsibility to be balanced and transparent in their use of evidence. In the classroom, learning how to analyze, interpret, and report data also needs to include explicit training in critical thinking skills, in which students explore the importance of context, assumptions, and bias. With this in mind, we integrate an innovative, multi-faceted pedagogical approach into an introductory statistics course, which incorporates writing assignments, small group discussion, and Socratic dialog. Our approach provides real-life applications for traditional statistical topics while also helping students learn to use data with integrity, ask important questions, and view problems holistically. [ABSTRACT FROM AUTHOR]

Like most professional disciplines, the ASA has adopted ethical guidelines for its practitioners. To promote these guidelines, as well as to meet governmental and institutional mandates, U.S. universities are demanding more training on ethics within existing statistics graduate student curricula. Most of this training is based on the teachings of Western philosophers. However, many statistics graduate students are from Eastern cultures (particularly Chinese), and cultural and linguistic evidence indicates that Western ethics may be difficult to translate into the philosophical concepts common to students from different cultural backgrounds. This article describes how to teach cross-cultural ethics, with emphasis on the ASA Ethical Guidelines, within a graduate-level statistical consulting course. In particular, we present content that can help students overcome cultural and language barriers to gain an understanding of ethical decision-making that is compatible with both Western and Eastern philosophical models. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

“Data science” is a useful catchword for methods and concepts original to the field of statistics, but typically being applied to large, multivariate, observational records. Such datasets call for techniques not often part of an introduction to statistics: modeling, consideration of covariates, sophisticated visualization, and causal reasoning. This article re-imagines introductory statistics as an introduction to data science and proposes a sequence of 10 blocks that together compose a suitable course for extracting information from contemporary data. Recent extensions to the mosaic packages for R together with tools from the “tidyverse” provide a concise and readable notation for wrangling, visualization, model-building, and model interpretation: the fundamental computational tasks of data science. [ABSTRACT FROM AUTHOR]

The six recommendations made by the Guidelines for Assessment and Instruction in Statistics Education (GAISE) committee were first communicated in 2005 and more formally in 2010. In this article, 25 introductory statistics textbooks are examined to assess how well these textbooks have incorporated the three GAISE recommendations most relevant to implementation in textbooks (statistical literacy and thinking; use of real data; stress concepts over procedures). The implementation of another recommendation (using technology) is described but not assessed. In general, most textbooks appear to be adopting the GAISE recommendations reasonably well in both exposition and exercises. The textbooks are particularly adept at using real data, using real data well, and promoting statistical literacy. Textbooks are less adept-but still rated reasonably well, in general-at explaining concepts over procedures and promoting statistical thinking. In contrast, few textbooks have easy-usable glossaries of statistical terms to assist with understanding of statistical language and literacy development. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The big data era demands new statistical analysis paradigms, since traditional methods often break down when datasets are too large to fit on a single desktop computer. Divide and Recombine (D&R) is becoming a popular approach for big data analysis, where results are combined over subanalyses performed in separate data subsets. In this article, we consider situations where unit record data cannot be made available by data custodians due to privacy concerns, and explore the concept of statistical sufficiency and summary statistics for model fitting. The resulting approach represents a type of D&R strategy, which we refer to assummary statistics D&R; as opposed to the standard approach, which we refer to ashorizontal D&R. We demonstrate the concept via an extended Gamma–Poisson model, where summary statistics are extracted from different databases and incorporated directly into the fitting algorithm without having to combine unit record data. By exploiting the natural hierarchy of data, our approach has major benefits in terms of privacy protection. Incorporating the proposed modelling framework into data extraction tools such as TableBuilder by the Australian Bureau of Statistics allows for potential analysis at a finer geographical level, which we illustrate with a multilevel analysis of the Australian unemployment data. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

The rootogram is a graphical tool associated with the work of J.W. Tukey that was originally used for assessing goodness of fit of univariate distributions. Here, we extend the rootogram to regression models and show that this is particularly useful for diagnosing and treating issues such as overdispersion and/or excess zeros in count data models. We also introduce a weighted version of the rootogram that can be applied out of sample or to (weighted) subsets of the data, for example, in finitemixture models. An empirical illustration revisiting a well-known dataset from ethology is included, for which a negative binomial hurdle model is employed. Supplementary materials providing two further illustrations are available online: the first, using data from public health, employs a two-component finite mixture of negative binomial models; the second, using data from finance, involves underdispersion. An R implementation of our tools is available in the R package countreg. It also contains the data and replication code. [ABSTRACT FROM AUTHOR]

In the past 20 years, there has been a staggering increase in the use of Bayesian statistical inference, based on Markov chain Monte Carlo (MCMC)methods, to estimate model parameters and other quantities of interest. This trend exists in virtually all areas of engineering and science. In a typical application, researcherswill report estimates of parametric functions (e.g., quantiles, probabilities, or predictions of future outcomes) and corresponding intervals from MCMC methods. One difficulty with the use of inferentialmethods based on Monte Carlo (MC) is that reported results may be inaccurate due to MC error. MC error, however, can be made arbitrarily small by increasing the number of MC draws. Most users of MCMC methods seem to use indirect diagnostics, trial-and-error, or guess-work to decide how long to run a MCMC algorithm and accuracy of MCMC output results is rarely reported. Unless careful analysis is done, reported numerical results may contain digits that are completely meaningless. In this article, we describe an algorithm to provide direct guidance on the number of MCMC draws needed to achieve a desired amount of precision (i.e., a specified number of accurate significant digits) for Bayesian credible interval endpoints. [ABSTRACT FROM AUTHOR]

Recalls statistical research papers previously published in the United States. Survey research; Sampling; Technical problems of statisticians at the U.S. Census Bureau.

I describe how developments over the past 25 years in computing, funding, personnel, purpose, and training have affected academic statistical consulting centers and discuss how these developments and trends point to a range of potential futures. At one extreme, academic statistical consulting centers fail to adapt to competition from other disciplines in an increasingly fragmented market for statistical consulting and spiral downward toward irrelevancy and extinction. At the other extreme, purpose-driven academic statistical consulting centers constantly increase their impact in a virtuous cycle, leading the way toward the profession of statistics having greater positive impact on society. I conclude with actions to take to assure a robust future and increased impact for academic statistical consulting centers. [ABSTRACT FROM AUTHOR]

The geographic distribution of nonprofit antipoverty organizations has important implications for economic development, social services, public health, and policy efforts. With counts of antipoverty nonprofits at the census tract level in Greater Hartford, Connecticut, we examine whether these organizations are located in areas with high levels of poverty with a spatial zero-inflated-Poisson model. Covariates that measure need, resources, urban structure, and demographic characteristics are incorporated into both the zero-inflation component and the Poisson component of the model. Variation not explained by the covariates is captured by the combination of a spatial random effect and an unstructured random effect. Statistical inferences are done within the Bayesian framework. Model comparison with the conditional predictive ordinate suggests that the random effects and the zero-inflation are both important components in fitting the data. All three need measures—proportion of people below the poverty line, unemployment rate, and rental occupancy—are found to have significantly positive effect on the mean of the count, providing evidence that antipoverty nonprofits tend to locate where they are needed. The dataset and R/OpenBUGS code are available in supplementary materials online. [ABSTRACT FROM AUTHOR]

In addition to his contributions to biostatistics and clinical trials, Paul Meier had a long-term interest in the legal applications of statistics. As part of this, he had extensive experience as a statistical consultant. Legal consulting can be a minefield, but as a result of his background, Paul had excellent advice to give to those starting out on how to function successfully in this environment. [ABSTRACT FROM AUTHOR]

The Ethical Guidelines for Statistical Practice of the American Statistical Association (ASA) have evolved over a span of more than 60 years, going back to 1949. The Interim version of the Guidelines was published in 1980, the Trial version was published in 1983 and revised and formalized in 1989, the current version was approved by the Board of Directors and made available on the ASA's Web site in 1999, and ASA accreditation now requires statistical practitioners to agree to abide by them. The new century brings new ethical concerns for statisticians. As examples, bioethics is booming, climate science is newsworthy for both science and ethics, and issues of statistical integrity in research keeps the U.S. Department of Health and Human Services Office of Research Integrity very busy. In this century, we see a rapid increase in the ability to collect massive amounts of data, with complex structure and a sometimes sensitive nature. With these unparalleled opportunities for statisticians comes an increased need for clear guidelines on professional ethics. The evolution of the Guidelines therefore needs to continue. In this article, we examine the long history of the ASA Ethical Guidelines for Statistical Practice, and discuss potential areas for revision to meet the needs of our expanding profession. [ABSTRACT FROM AUTHOR]

This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA designs is useful for empirical researchers and for students; both groups will get a more acute appreciation of Bayesian inference when they can apply it to practical statistical problems such as ANOVA. We illustrate the use of the test with two examples, and we provide R code that makes the test easy to use. [ABSTRACT FROM AUTHOR]

An enormously productive 18th century mathematician, Euler made a number of wide-ranging advances that remain useful in statistics. These include beta and gamma functions, summation of infinite series, calculus of variations, Euler's phi-function, Latin and Graeco-Latin squares, and generating functions. All these are outlined and illustrated in this article. [ABSTRACT FROM AUTHOR]

Matching is a powerful statistical tool in design and analysis. Conventional two-group, or bipartite, matching has been widely used in practice. However, its utility is limited to simpler designs. In contrast, nonbipartite matching is not limited to the two-group case, handling multiparty matching situations. It can be used to find the set of matches that minimize the sum of distances based on a given distance matrix. It brings greater flexibility to the matching design, such as multigroup comparisons. Thanks to improvements in computing power and freely available algorithms to solve nonbipartite problems, the cost in terms of computation time and complexity is low. This article reviews the optimal nonbipartite matching algorithm and its statistical applications, including observational studies with complex designs and an exact distribution-free test comparing two multivariate distributions. We also introduce an R package that performs optimal nonbipartite matching. We present an easily accessible web application to make nonbipartite matching freely available to general researchers. [ABSTRACT FROM AUTHOR]

The recent article by Meng (2009a) continues a long tradition of articles in this journal dealing with the future of statistics and statisticians. For over 25 years many of these accounts painted varying shades of the same grim picture—that our continued existence is under threat; the challenges are great; respect has been in short supply; and our future is bleak. In this article I suggest we spend less time scanning the cross-disciplinary borders for new intrusions and rather than shoring up the fortress, we open up the borders. I share Meng’s upbeat enthusiasm for a bright future while recognizing much remains to be done to increase our relevance and effectiveness. [ABSTRACT FROM AUTHOR]

Conventional practice is to draw inferences from all available data and research results. When a scientific literature is plagued by publication selection bias, a simple discarding of the vast majority of empirical results can actually improve statistical inference and estimation. Simulations demonstrate that, if statistical significance is used as a criterion for reporting or publishing estimates, discarding 90% of the published findings greatly reduces publication selection bias and is often more efficient than conventional summary statistics. Improving statistical estimation and inference through removing so much data goes against statistical theory and practice; hence, it is paradoxical. We investigate a very simple method to reduce the effects of publication bias and to improve the efficiency of summary estimates of accumulated empirical research results that averages the most precise 10% of the reported estimates (i.e., ‘ Top10’). In the process, the critical importance of precision (the inverse of an estimate’s standard error) as a measure of a study’s quality is brought to light. Reviewers and journal editors should use precision, when possible, as one objective measure of a study’s quality. [ABSTRACT FROM AUTHOR]

The Mar Saba letter is a controversial document purportedly written by the second century theologian Clement of Alexandria and containing excerpts from the so-called "Secret Gospel" of Mark. There is evidence that the Mar Saba letter is a forgery. This evidence includes a statistical analysis comparing the vocabulary of the Mar Saba letter with that of the acknowledged works of Clement. This statistical analysis, which was based on Herbert A. Simon's model of text generation, was casually reported. Here a statistical framework for this analysis is presented. As a by-product, a more powerful test of authenticity is described. [ABSTRACT FROM AUTHOR]

An intense debate about Harvard University's General Education Curriculum demonstrates that statistics, as a discipline, is now both desired and feared. With this new status comes a set of enormous challenges. We no longer simply enjoy the privilege of playing in or cleaning up everyone's backyard. We are now being invited into everyone's study or living room, and trusted with the task of being their offspring's first quantitative nanny. Are we up to such a nerve-wracking task, given the insignificant size of our profession relative to the sheer number of our hosts and their progeny? Echoing Brown and Kass's "What Is Statistics?" (2009), this article further suggests ways to prepare our profession to meet the ever-increasing demand, in terms of both quantity and quality. Discussed are (1) the need to supplement our graduate curricula with a professional development curriculum (PDC); (2) the need to develop more subject oriented statistics (SOS) courses and happy courses at the undergraduate level; (3) the need to have the most qualified statisticians—in terms of both teaching and research credentials—to teach introductory statistical courses, especially those for other disciplines; (4) the need to deepen our foundation while expanding our horizon in both teaching and research; and (5) the need to greatly increase the general awareness and avoidance of unprincipled data analysis methods, through our practice and teaching, as a way to combat "incentive bias," a main culprit of false discoveries in science, misleading information in media, and misguided policies in society. [ABSTRACT FROM AUTHOR]

In many branches of modern science, researchers first study or mine large datasets, and then select the parameters they estimate and the data they use and publish. Such data-based selection complicates formal statistical inference. An example discussed here for the purpose of illustration, is that of pharmaceutical companies that typically conduct many experiments but may publish only selected data. The selection often depends on the outcomes of the experiments since naturally there is interest in potentially useful drugs, and it is in general unclear how it should affect inference. Is this effect the same for the company and the public? Does it matter if they are Bayesian or frequentist? Should the company reveal all experiments it conducts and, if so, how should this change the conclusions? This note discusses these questions in terms of a simple example of a sequence of binomial experiments conducted by a pharmaceutical company, where results are published only if the number of "failures" is small. We do not suggest that this example corresponds to reality in the pharmaceutical industry, nor in science in general; our goal is to elaborate on the importance and difficulties of taking selection into account when performing statistical analysis. [ABSTRACT FROM AUTHOR]

Judging bias is an inherent risk in subjectively judged sporting competitions, and recent controversies have spurred re- searchers to explore these biases wherever possible. Unfortunately, detailed judging results are usually unavailable to the public. For example, the international figure skating scoring system does not allow the study of nationalistic bias, because the scores are reported anonymously. Similarly, the National Basketball Association (NBA) blocked requests for underlying data after a recent academic study of racial bias of NBA referees. This article makes use of a rare case of fully available judging data, examining the diving competitions from the 2000 Summer Olympic Games. We discover strong evidence of nationalistic favoritism in the judging, including one case where the medal standings reasonably could have changed with unbiased judging. We offer a simple framework on which to base future studies of judging bias. [ABSTRACT FROM AUTHOR]

Digital learning material was developed to help students acquire the necessary skills for the preparation and implementation of a data analysis protocol. The learning material focused on the relation between the main experimental and observational study types in human nutrition research and the appropriate methods of data analysis. This article describes the main guidelines derived from theories on learning and instruction and illustrates how they were used to develop the digital learning material. Evaluation of the learning material in an academic context indicated that students appreciated the learning material and achieved the learning objectives. [ABSTRACT FROM AUTHOR]

The traditional statistical approach to the evaluation of diagnostic tests, prediction models, and molecular markers is to assess their accuracy, using metrics such as sensitivity, specificity, and the receiver-operating-characteristic curve. However, there is no obvious association between accuracy and clinical value: it is unclear, for example, just how accurate a test needs to be in order for it to be considered "accurate enough" to warrant its use in patient care. Decision analysis aims to assess the clinical value of a test by assigning weights to each possible consequence. These methods have been historically considered unattractive to the practicing biostatistician because additional data from the literature, or subjective assessments from individual patients or clinicians, are needed in order to assign weights appropriately. Decision analytic methods are available that can reduce these additional requirements. These methods can provide insight into the consequences of using a test, model, or marker in clinical practice. [ABSTRACT FROM AUTHOR]

Since their emergence in the 1990s, the support vector ma- chine and the AdaBoost algorithm have spawned a wave of research in statistical machine learning. Much of this new research falls into one of two broad categories: kernel methods and ensemble methods. In this expository article, I discuss the main ideas behind these two types of methods, namely how to transform linear algorithms into nonlinear ones by using kernel functions, and how to make predictions with an ensemble or a collection of models rather than a single model. I also share my personal perspectives on how these ideas have influenced and shaped my own research. In particular, I present two recent algorithms that I have invented with my collaborators: LAGO, a fast kernel algorithm for unbalanced classification and rare tar- get detection; and Darwinian evolution in parallel universes, an ensemble method for variable selection. [ABSTRACT FROM AUTHOR]

Assessment has become the "buzzword" in academia; a demonstration of criteria used for the assessment of retention of what was learned is now mandated by various accrediting agencies. Whether we want our students to be good users of statistics who make better decisions, or good consumers of statistics who are better informed citizens, we must reflect on how key statistical concepts can be ingrained in the students' knowledge base. This article seeks to address the overall issue of assessing the retention of essential statistical ideas that transcend various disciplines. [ABSTRACT FROM AUTHOR]

In this article, we consider the problem of constructing confidence intervals for a population median when the underlying population is discrete. We describe seven methods of assigning confidence levels to order statistic based confidence intervals, all of which are easy to implement. A simulation study shows that, with discrete populations, it is possible to obtain consistently more accurate confidence levels and shorter intervals compared to the ones reported by the classical method which is implemented in commercial software. More precisely, the best results are obtained by inverting a two-tailed sign test that properly takes into account tied observations. Some real data examples illustrate the use of these confidence intervals. [ABSTRACT FROM AUTHOR]

Our goal is to provide a much needed reminder that the negative hypergeometric distribution is unnecessarily omitted from discussion in probability and statistics courses. By considering the binomial, hypergeometric, negative binomial, and negative hypergeometric distributions collectively rather than in disjoint fashion, students can better understand the applicability of each model. From a pedagogical point of view, we propose that the relationships between these models are best understood by considering them together rather than singly. [ABSTRACT FROM AUTHOR]

Bagging (bootstrap aggregating) is a procedure that aims at reducing the prediction error of a classifier or the mean square error of an estimate by averaging its values across bootstrap samples. We illustrate some of the effects of bagging on point estimation using only averages and medians. Our examples show that when we compute the bagged version of a robust estimate, the size of the bootstrap samples can be viewed as a tuning constant that controls the trade-off between efficiency and robustness. To quantify the robustness properties of bagged estimates we introduce a new concept of breakdown point that is useful in situations when resampling is needed. Finally, a robust version of bagging applied to the average leads to generalizations of previous results about the Hodges-Lehmann estimate. [ABSTRACT FROM AUTHOR]

This article comprehensively reviews the recent controversial book The Black Swan: Impact of the Highly Improbable. Whereas the book is statistically reckless about many issues in our profession, I also find reason to recommend perusal of its contents. [ABSTRACT FROM AUTHOR]

Kappa-type indices use the concept of agreement to express the reproducibility of nominal measurements. This article grounds kappa-type indices in statistical modeling, making explicit the underlying premises and assumptions. We critically review whether the interpretation of the kappa index as a chance-corrected probability of agreement can be substantiated. Further, we show that the so-called paradoxical behavior of the kappa index is explained from the fact that it is a measure of predictive association, rather than a pure measure of reproducibility. We discuss a number of alternative forms, critically examining whether they can be translated in tangible real-life interpretations. [ABSTRACT FROM AUTHOR]

The rapid development of biological technology in recent years has generated an overwhelming quantity of highly complex genetical data. In retrospect it is perhaps now obvious that standard stochastic sampling algorithms were destined to fail when applied to the type of models that these data require. During this period, both the genetics and statistics research communities have developed a range of new algorithms that have endeavored to avoid these failures. This article presents a brief journey through the development of some of these algorithms. I hope to demonstrate that continued collaborative research and education in both genetics and statistics disciplines is crucial for the advancement of future sampling frameworks, and call on researchers in both fields to take up this challenge. [ABSTRACT FROM AUTHOR]