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In the Anglo-American legal system, courts rely heavily on experts who perform an essential social function in supplying information to resolve disputes. Experts are the vehicles through which facts of any technical complexity are brought out. The adversarial nature of this legal system places expert witnesses in a quandary. Enjoined to serve the court and their profession with unbiased, independent opinion, expert witnesses nevertheless do not work directly for the court: they are employed by advocates (lawyers) who aim to win a high stakes debate for their clients. The system is imperfect. Pressures (whether real or perceived) on experts to please their clients may cause truth to be the victim. We use examples from our experience, and reports of statisticians commenting on theirs, to show how statistical evidence can be honestly and effectively used in courts. We maintain it is vital for would-be experts to study the rules of the legal process and their role within it. (The present article is a step toward that end.) We explain what the legal process looks for in an expert and present some ways in which an expert can maintain their independence and avoid being co-opted by the lawyer who sponsors them. Statisticians contribute in sometimes unique ways to the resolution of disputes, including in forums like negotiations, mediation, arbitration, and regulatory hearing, where the misuse and abuse of statistical procedures occur too often. It is a challenge for statisticians to improve that situation, but they can find professional opportunities and satisfaction in doing so. Because this discussion pertains generally to the application and communication of statistical thinking, statisticians in any sphere of application should find it useful. [ABSTRACT FROM AUTHOR]

Agent-based models (ABMs) are popular in many research communities, but few statisticians have contributed to their theoretical development. They are models like any other models we study, but in general, we are still learning how to fit ABMs to data and how to make quantified statements of uncertainty about the outputs of an ABM. ABM validation is also an underdeveloped area that is ripe for new statistical developments. In what follows, we lay out the research space and encourage statisticians to address the many research issues in the ABM ambit. [ABSTRACT FROM AUTHOR]

We explore the arguments for maximizing the "evidence" as an algorithm for model selection. We show, using a new definition of model complexity which we term "flexibility," that maximizing the evidence should appeal to both Bayesian and frequentist statisticians. This is due to flexibility's unique position in the exact decomposition of log-evidence into log-fit minus flexibility. In the Gaussian linear model, flexibility is asymptotically equal to the Bayesian information criterion (BIC) penalty, but we caution against using BIC in place of flexibility for model selection. [ABSTRACT FROM AUTHOR]

In 2003, Holmes [The Statistician 52, Part 4, (2003), 439–474] reviewed 50 years of teaching statistics in English schools and drew out many lessons for more effective teaching. We consider the current relevance of several of these lessons and whether we have learned from them. Many national reports have been published about teaching statistics with few of their recommendations being implemented. There have been rapid developments in the methodology of, and technology used by, statistics at all levels outside schools. These have led to the accelerated development of the use of statistics and related disciplines such as data science (DS). In schools, we have not seen as much progress in teaching, learning, assessing, or communicating these subjects. We consider reasons for lack of progress in pedagogy and make our own recommendations to get schools to catch up with the exciting developments in the practice and communication of statistics and DS. [ABSTRACT FROM AUTHOR]

The article presents a methodography of a collaborative design workshop conducted with national and international statisticians. The workshop was part of an ethnographic research project on innovation in European official statistics. It aimed to bring academic researchers and statisticians together to collaborate on the design of app prototypes that imagine citizens as co-producers of official statistics rather than only data subjects. However, the objective was not to settle on an end product but to see if relations to citizens could be re-imagined. Through a methodography composed of two ethnographic narratives, we analyse whether and how a collaborative design workshop brought about imaginings of citizens as co-producers. To retrospectively analyse the workshop, we draw on feminist and materialsemiotic takes on 'friction' as characteristic of collaboration. 'Friction', we suggest, can enlarge the repertoire of collaborative speculative practice beyond notions of rupture or consensus. Finally, we suggest that this analysis demonstrates the potential of methodography for opening up and reflecting on method in STS through eliciting the possibilities of collaboration. [ABSTRACT FROM AUTHOR]

Statistics is undergoing what feels like an evolutionary jump, i.e., a period of rapid and sweeping developments brought about by dramatic shifts in its environment. The repercussions are felt strongly by official statistics, which operates at the forefront of societal and economic change. In this paper, we look at the implications of the recent developments for the training of official statisticians and highlight key knowledge areas for successfully navigating the emerging landscape. In addition, we employ the concept of adaptive expertise to help us identify three qualities that support the independent and lifelong development of practicing statisticians, and propose five teaching strategies for fostering these qualities in the classroom. [ABSTRACT FROM AUTHOR]

The training offer of official statistics in statistical training institutes has been constantly evolving as it adapts to the statistical environment and technological developments. Based on a literature review and the mobilisation of curricula and programmes offered by statistical training centres in Africa, this paper presents the current situation of training in official statistics as well as the challenges to be faced. Despite harmonisation efforts, there are still differences between language areas and training types. Engineer and vocational statistical training are better suited to the needs of National Statistical Institutes than university training. It is essential that the training of statisticians is strategically thought out so that they can be reactive and dynamic in the face of changes and upheavals they will be confronted with in the context of data revolution and big data. Their training should reinforce the statistical literacy dimension with a view to reducing the gap between producers and users. [ABSTRACT FROM AUTHOR]

In this paper, we follow the work [Held, L., and M. Ott. 2016. How the maximal evidence of p-values against point null hypotheses depends on sample size. The American Statistician 70 (4):335–41] and propose a sample-size adjusted minimum Bayes factor (minBF) for testing the presence of a correlation or a partial correlation. The proposed minBF is related to the two-sided p-value from the frequentist test and can be easily calculated using either a pocket calculator or spreadsheets, so long as the researcher is familiar with the frequentist paradigm. It turns out that the minBF increases with an increasing sample size, which implies that the maximal evidence of the two-sided p-value decreases with an increasing sample size. Simulation studies and two real-data applications are provided for illustrative purposes. [ABSTRACT FROM AUTHOR]

The gtsummary package provides an elegant and flexible way to create publication-ready summary tables in R. A critical part of the work of statisticians, data scientists, and analysts is summarizing data sets and regression models in R and publishing or sharing polished summary tables. The gtsummary package was created to streamline these everyday analysis tasks by allowing users to easily create reproducible summaries of data sets, regression models, survey data, and survival data with a simple interface and very little code. The package follows a tidy framework, making it easy to integrate with standard data workflows, and offers many table customization features through function arguments, helper functions, and custom themes. [ABSTRACT FROM AUTHOR]

The world has experienced three global pandemics over the last half-century: HIV/AIDS, H1N1, and COVID-19. HIV/AIDS and COVID-19 are still with us and have wrought extensive havoc worldwide. There are many differences between these two infections and their global impacts, but one thing they have in common is the mobilization of scientific resources to both understand the infection and develop ways to combat it. As was the case with HIV, statisticians have been in the forefront of scientists working to understand transmission dynamics and the natural history of infection, determine prognostic factors for severe disease, and develop optimal study designs to assess therapeutics and vaccines. [ABSTRACT FROM AUTHOR]

In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents. [ABSTRACT FROM AUTHOR]

This study aims to present those foreign economic and statistical theories which had significant impact on the concepts of Károly Keleti, the first head of the official Hungarian statistical service established in 1867, and to examine the influence of this international experience on his economic works, ideas on statistics, and principles related to the organisation of the Hungarian statistical office. This topic has not been sufficiently addressed in the Hungarian literature; thus, the author's research provides new information not only on the formation of the national statistical system after the Austro-Hungarian Compromise but also on the origins of the economic and statistical views of one of the most prominent Hungarian economic writers and statisticians in the 19th century. The study focuses on the close intertwinement of Keleti's economic and statistical work, its causes and consequences. [ABSTRACT FROM AUTHOR]

Collaborative work is inherent to being a statistician or data scientist, yet opportunities for training and exposure to real-world scenarios are often only a small part of a student's academic program. Resources to facilitate effective and meaningful instruction in communication and collaboration are limited, particularly when compared to the abundant resources available to support traditional statistical training in theory and methods. Our work helps fill the need for resources by providing ten modern, freely-available videos of mock collaborative interactions, with supporting discussion questions, scripts, and other resources. Videos are particularly helpful for teaching communication dynamics. These videos are set in the context of academic research discussions, though the scenarios are broad enough to facilitate discussions for other collaborative contexts as well. The videos and associated resources are designed to be incorporated into existing curricula related to collaboration. The materials have been piloted with positive feedback from students and instructors. Supplemental files for this article are available online. [ABSTRACT FROM AUTHOR]

Summary: As sources of data become more plentiful and massive datasets are easier to acquire, new ethical issues arise involving data quality and privacy, and the analysis, interpretation and dissemination of data‐driven decisions. There are numerous anecdotes involving abuses of complex data analyses and algorithms, and the impact they have had on society. In this paper, we discuss what statisticians can do to help enhance data science ethics in practice and what statistics educators can do to instil sound ethical behaviour in our students. We have opportunities to practice and teach ethical conduct relevant to all stages of the data life cycle. This paper discusses issues impacting ethical data science, with a focus on how statisticians can help raise awareness and encourage implementation of ethical best practices. [ABSTRACT FROM AUTHOR]

We study the consequences of alphabetical author order in economics for job placements of economists, based on a context that enables a clean identification. Results using two different data sets both show that, relative to Chinese physicists and statisticians, Chinese economists with surname initials located later in the alphabet tend not to stay in the US and are more likely to work in China. Such effects of surname initials on job placements are clearly identified and represent a result of alphabetical author order, because in Chinese culture, names are seldom listed alphabetically in contexts other than joint publications in international economics journals. [ABSTRACT FROM AUTHOR]

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Using both sources of information, we construct a temporal author citation network as a series of timestamped directed edges, where nodes represent authors and a directed timestamped edge pointing from a citer to a citee represents that an author cited at least one article from another author within a given year. We thank the authors for their new contribution to a high quality dataset and interesting findings from the modeling and analysis of the co-citation and co-authorship networks of statisticians. Definition of Temporal Author Citation Networks The co-citation and co-authorship networks studied in the article are in the form of matrices or time series of matrices. [Extracted from the article]

Preservation of digital material is a challenge for which many archives feel underprepared and ill equipped. The National Archives (UK) has been working in collaboration with statisticians from the University of Warwick and partners from across the UK archives sector to develop a decision-support system which quantifies the risks involved in digital preservation. Through interdisciplinary collaboration, this partnership has developed an interactive tool for managing risks to digital material, based on a Bayesian statistical network. The tool provides archivists with a different way of thinking about digital preservation, supported by an evidence base they can use to advocate for action. The project illustrates the potential benefit of a collaborative approach, combining insight from different disciplines. [ABSTRACT FROM AUTHOR]

This paper comprises a review of the Pocket Study Skills book How to Analyze Data which I was invited by MacMillan International Higher Education to provide as a contribution to the current special issue. This includes both a preliminary review and an extension of this review to provide ten tips for educators to enhance the statistical learning of non-specialists in statistics. The tips are intended to provide a constructive approach to the review process through safeguarding educators, including dissertation supervisors, and learners, from being misled by some of the shortcomings that I have identified in this book. It is in this sense that I refer to these tips as "remedial tips". As such, these tips should not be regarded as exhaustive in their own right in reflecting the statistical learning needs of non-specialists. Nevertheless, one of the important responsibilities professionally trained statisticians face in the teaching of statistics to non-specialists is that of empowering learners to stem the tide of misuse of statistics within their own disciplines, and one of the corresponding challenges is that of identifying published material that is potentially unhelpful and even counterproductive in achieving this goal. Thus, it is also my intention that these tips will be of broader pedagogical value as a handy reference, including for those uninitiated teachers of statistics to non-specialists who may be tempted to reach out for a pocket guide to support them in balancing the demands of teaching and research in an effort to make statistics less mystifying to their students. [ABSTRACT FROM AUTHOR]

The author argues that Sufficient attention has not been paid by economists and statisticians to the fact that, given the way productivity indicators at sector and country-level are defined and determined by statistical agencies, the resulting figures—for comparisons among countries, or among sectors within a country, as well as for comparisons over time ("productivity growth")—do not actually have the assumed meaning: that is, an increase of the index indicates that the country's (sector's) companies have become more productive, in the sense of producing more products or services with the same quantities of resources, or producing the same with fewer quantities of resources, etc. [ABSTRACT FROM AUTHOR]

One of the main sources of non-sampling errors is missing data due to non-response. Various methods are adopted to deal with this problem, one of which is the method of imputation. In this paper, a few logarithmic and sine type imputation techniques have been proposed for estimating the population mean and the effect of measurement errors on the resultant estimators has been examined. Their properties in terms of bias and mean square errors have been studied. Empirical studies have been carried out on real and simulated data sets to show the efficiency of the proposed estimators over contemporary estimators. Suitable recommendations have been put forward to the survey statisticians for applications in real-life problems. [ABSTRACT FROM AUTHOR]

In criminal trials, evidence often involves a degree of uncertainty and decision-making includes moving from the initial presumption of innocence to inference about guilt based on that evidence. The jurors' ability to combine evidence and make accurate intuitive probabilistic judgments underpins this process. Previous research has shown that errors in probabilistic reasoning can be explained by a misalignment of the evidence presented with the intuitive causal models that people construct. This has been explored in abstract and context-free situations. However, less is known about how people interpret evidence in context-rich situations such as legal cases. The present study examined participants' intuitive probabilistic reasoning in legal contexts and assessed how people's causal models underlie the process of belief updating in the light of new evidence. The study assessed whether participants update beliefs in line with Bayesian norms and if errors in belief updating can be explained by the causal structures underpinning the evidence integration process. The study was based on a recent case in England where a couple was accused of intentionally harming their baby but was eventually exonerated because the child's symptoms were found to be caused by a rare blood disorder. Participants were presented with a range of evidence, one piece at a time, including physical evidence and reports from experts. Participants made probability judgments about the abuse and disorder as causes of the child's symptoms. Subjective probability judgments were compared against Bayesian norms. The causal models constructed by participants were also elicited. Results showed that overall participants revised their beliefs appropriately in the right direction based on evidence. However, this revision was done without exact Bayesian computation and errors were observed in estimating the weight of evidence. Errors in probabilistic judgments were partly accounted for, by differences in the causal models representing the evidence. Our findings suggest that understanding causal models that guide people's judgments may help shed light on errors made in evidence integration and potentially identify ways to address accuracy in judgment. [ABSTRACT FROM AUTHOR]

In stratified random sampling, Ahsan et al. [Mixed allocation in stratified sampling. Aligarh J Stat. 2005;25:87–97] introduced the concept of 'Mixed allocation' for fixed cost by minimizing the variance of the stratified sample mean which was based on the work of Clark and Steel [Optimum allocation of sample to strata and stages with simple additional constraints. Statistician. 2000;49(2):197–207] for two-stage univariate sampling design. The present manuscript studies the problem of obtaining a 'compromise mixed allocation' in multivariate stratified sampling using Fuzzy Programming Technique. To exhibit the application of the defined approach, a numerical illustration is considered and solved using Lingo 13.0. In order to demonstrate the superiority of proposed approach, the obtained results have been compared with four other existing approaches. [ABSTRACT FROM AUTHOR]

From above, we know that ( I r i SB I Cs i 1 sb , I r i SB I Cs i 2 sb ) equals either HT ht or HT ht . Then we can write Wilcoxon's signed rank statistic as HT ht . The expectation and variance are analogous for I T i SP - sp expect that I p i SP + sp is replaced with HT ht . [Extracted from the article]

Professor Sir Austin Bradford Hill's lecture on "The Statistician in Medicine" was a pleasure to read, and provided many points upon which to reflect. Indeed, in quoting the Annual Report of the Royal Statistical Society of 1849, Bradford Hill reminds us that being a statistician requires far more than that foundation: we know no statistician who aspires solely to be "a very able calculator." Sir Bradford Hill's lecture inspires a whole other response: we are teaching younger minds to enquire, to question, to be ethical, and to reason; to read and learn widely, and to go far beyond data artistry to query and conclude from data drawn from the world in which we live. [Extracted from the article]

In this commentary, we revisit Sir Austin Bradford Hill's seminal Alfred Watson Memorial Lecture in 1962 through the eyes of two practicing biostatisticians of the current era. We summarize some eternal takeaway messages from Hill's lecture regarding observations and experiments translated through the modern lexicon of causal inference. Finally, we pose a series of questions that we would have liked to pose to Sir Austin Bradford Hill if he were to deliver the lecture in 2020. [ABSTRACT FROM AUTHOR]

The "replication crisis" has been attributed to perverse incentives that lead to selective reporting and misinterpretations of P-values and confidence intervals. A crude fix offered for this problem is to lower testing cut-offs (α levels), either directly or in the form of null-biased multiple comparisons procedures such as naïve Bonferroni adjustments. Methodologists and statisticians have expressed positions that range from condemning all such procedures to demanding their application in almost all analyses. Navigating between these unjustifiable extremes requires defining analysis goals precisely enough to separate inappropriate from appropriate adjustments. To meet this need, I here review issues arising in single-parameter inference (such as error costs and loss functions) that are often skipped in basic statistics, yet are crucial to understanding controversies in testing and multiple comparisons. I also review considerations that should be made when examining arguments for and against modifications of decision cut-offs and adjustments for multiple comparisons. The goal is to provide researchers a better understanding of what is assumed by each side and to enable recognition of hidden assumptions. Basic issues of goal specification and error costs are illustrated with simple fixed cut-off hypothesis testing scenarios. These illustrations show how adjustment choices are extremely sensitive to implicit decision costs, making it inevitable that different stakeholders will vehemently disagree about what is necessary or appropriate. Because decisions cannot be justified without explicit costs, resolution of inference controversies is impossible without recognising this sensitivity. Pre-analysis statements of funding, scientific goals, and analysis plans can help counter demands for inappropriate adjustments, and can provide guidance as to what adjustments are advisable. Hierarchical (multilevel) regression methods (including Bayesian, semi-Bayes, and empirical-Bayes methods) provide preferable alternatives to conventional adjustments, insofar as they facilitate use of background information in the analysis model, and thus can provide better-informed estimates on which to base inferences and decisions. [ABSTRACT FROM AUTHOR]

Charles Sanders Peirce wrote the article 'The probability of induction' in 1878. It was the fourth article of the series 'Illustrations of the Logic of Science' which comprised a total of six articles. According to Peirce, to get a clear idea of the conception of probability, one has 'to consider what real and sensible difference there is between one degree of probability and another.' He endorsed what John Venn had called the 'materialistic view' of the subject, namely that probability is the proportion of times in which an occurrence of one kind is accompanied by an occurrence of another kind. On the other hand, Peirce recognized the existence of a different interpretation of probability, which was termed by Venn the 'conceptualistic view,' namely the degree of belief that ought to be attached to a proposition. Peirce's intent on writing this article seems to be to inquire about the claims of the conceptualists concerning the problem of induction. After reasoning on some examples, he concluded on the impossibility of assigning probability for induction. We show here that the arguments advanced in his article are not sufficient to support such conclusion. Peirce's thoughts on the probability of induction surely may have influenced statisticians and research scientists of the twentieth century in shaping data analysis. [ABSTRACT FROM AUTHOR]

PURPOSE: The aims of this Erasmus+ are to establish an academic partnership, to exchange practice in teaching and research. In September 2016 was signed an inter-institutional agreement for staff mobility for teaching between the University of Southampton, UK and Trakia University, Bulgaria for the period 2016-2021. Assoc. Prof. Dr Borislav Dimitrov, a former colleague from Stara Zagora working at the University of Southampton and Assoc. Prof. Katya Peeva from the Medical faculty were the contact persons for this contract. RESULTS: Until the year 2020 in the program have participated six professors from the University of Southampton, who delivered lectures and courses to teaching staff at Trakia University “Introduction into Biostatistics with R” and “Medical Statistics using SPSS”. From the Medical Faculty the participants were four professors with lectures, seminars, posters and taking part as a teaching assistant in a statistical course at the University of Southampton. CONCLUSIONS: Assoc. Prof. Dr Borislav Dimitrov created a fantastic opportunity. The plus in Erasmus+ signifies learning as more than just an academic exercise. I'm sure that Borislav would have hoped we learn that academic endeavour and partnership could be an area able to rise above the differences between individual countries. [ABSTRACT FROM AUTHOR]