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AbstractHeavy tails (HT) arise in many applications and their presence can disrupt statistical inference, yet the HT statistical literature requires a theoretical background most practicing statisticians lack. We provide an overview of the influence of HT on the performance of basic statistical methods and useful theorems aimed at the practitioner encountering HT in an applied setting. Higher or even lower product moments (i.e., variance, skewness, etc.) can be infinite for some HT populations, yet all L-moments are always finite, given that the mean exists, thus, the theory of L-moments is uniquely suited to all HT distributions and data. We document how L-kurtosis, (a kurtosis measure based on the fourth L-moment) provides a general and practical heaviness index for contrasting tail heaviness across distributions and datasets and how a single L-moment diagram can document both the prevalence and impact of HT distributions and data across disciplines and datasets. Surprisingly, the theory of L-moments, an extension and evolution of probability weighted moments, has been largely overlooked by the literature on HT distributions that exhibit infinite moments. Experiments reveal L-kurtosis ranges under which various HT distributions result in mild to severe disruption to the bootstrap, the central limit theorem (CLT), and the law of large numbers, even for distributions which exhibit finite product moments. [ABSTRACT FROM AUTHOR]

In recent years, researchers and statisticians have increasingly used econometric techniques to generate timely, high-quality, and detailed official statistics. This study presents a modified form of regression-based temporal disaggregation model to compile the index of production in construction for Türkiye, employing a state-space modeling approach. The model incorporates the twelve-month moving sum of deflated turnover as an observed variable and the number of employees as an exogenous variable. Finally, four alternative models--three assuming constant labor productivity and one assuming time-varying labor productivity--are compared. [ABSTRACT FROM AUTHOR]

Background and Study Aim The attack is a crucial technical element in scoring points in volleyball. This research aims to identify differences in attack efficiency and errors based on the value level of teams in the competitive system, considering different playing positions. Material and Methods The analysis included 86 attacking players from the rosters of 12 teams in the men's domestic first league. The distribution by positions was as follows: 37 outside hitters, 19 opposites, and 30 middle blockers. Statisticians from each team recorded the data using Data Volley software during three championship matches. For each player, the following parameters were interpreted: Attack Efficiency (E%), Error% (=), Blocked Attack% (/), Poor% (-), Blocked but Recovered% (!), Positive% (+), and Winning% (#). Attack efficiency was analyzed for each playing position across top teams (positions 1-4 in the final ranking), mid-level teams (positions 5-8), and lower-level teams (positions 9-12). Results Across all three playing positions, top teams demonstrate higher percentages in attack efficiency, winning executions, and positive outcomes compared to average and lower-level teams. Top and average teams also have lower percentages of errors in attack executions. For the outside hitter position, no statistically significant superiority is observed between top and middle teams for any attack variable, indicating a balance in player performance. In the opposite hitter position, top and middle-level players show higher attack efficiency and direct point scores (Winning). They also have lower poor execution rates, despite the lack of statistical confirmation for superiority in many attack variables. Among all positions, top middle blockers exhibit a clear superiority over low-level teams across all attack variables. Conclusions The findings of the study highlight the critical role of team value level in influencing attack efficiency and execution quality across different playing positions in volleyball. The results underscore the importance of strategic differentiation in training and game planning. This is especially crucial for teams aiming to optimize their performance. Coaches and trainers should focus on tailored approaches that address the specific strengths and weaknesses of each playing position. They should also consider the overall team value level in their strategies. This approach could lead to improved competitive outcomes and a more effective utilization of player potential across all levels of competition. [ABSTRACT FROM AUTHOR]

Copyright of Polish Statistician / Wiadomości Statystyczne is the property of State Treasury - Statistics Poland and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: scapular dyskinesis and scapular muscle weakness are condition associated with upper limb activity, poor posture, sustained manual labor. This cause discomfort and pain. Sugar factory workers frequently engage in repetitive activity that can cause scapular dyskinesis. Methods: The cross-sectional survey that was conducted in Ajinkya tara sugar factory satara the purpose of this repetitive study is to determine how many sugar factory workers are affected with scapular dyskinesis. Prior to conducting the survey, the ethical committee approval was made. Patient select according to inclusion and exclusion criteria. With permission.172 sugar factory workers are participating in this study. And result was obtained by the help of statistician Result: in this study we found 128 sugar factory workers are positive for scapular dyskinesis the percentage of prevalence of this study was 74.41%and also these workers are present with scapular muscle weakness and present with shoulder pain. Conclusion: this study indicates that SD is prevalent in sugar factory worker. Those are engaged in repetitive overhead activity and heavy lifting. And also, scapular muscle weakness seen in this people. [ABSTRACT FROM AUTHOR]

Modern statistics provides an ever-expanding toolkit for estimating unknown parameters. Consequently, applied statisticians frequently face a difficult decision: retain a parameter estimate from a familiar method or replace it with an estimate from a newer or more complex one. While it is traditional to compare estimates using risk, such comparisons are rarely conclusive in realistic settings. In response, we propose the "c-value" as a measure of confidence that a new estimate achieves smaller loss than an old estimate on a given dataset. We show that it is unlikely that a large c-value coincides with a larger loss for the new estimate. Therefore, just as a small p-value supports rejecting a null hypothesis, a large c-value supports using a new estimate in place of the old. For a wide class of problems and estimates, we show how to compute a c-value by first constructing a data-dependent high-probability lower bound on the difference in loss. The c-value is frequentist in nature, but we show that it can provide validation of shrinkage estimates derived from Bayesian models in real data applications involving hierarchical models and Gaussian processes. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

As the New Zealand government is progressing plans for substituting the census as we know it for information held in state agency records, the huge gap between what we need population statistics for, and the scope and quality of existing statistics stays under the radar. The scale and volatility of migration, the impact of increased longevity and the different demographic dynamics of fast-growing ethnic communities must be continually measured in ways we can trust. Confidence that the statistical qualities of the census would be replicated requires transparency, validation and independent peer review if trust is to be maintained. [ABSTRACT FROM AUTHOR]

The rise of internet-based services and products in the late 1990s brought about an unprecedented opportunity for online businesses to engage in large scale data-driven decision making. Over the past two decades, organizations such as Airbnb, Alibaba, Amazon, Baidu, Booking.com, Alphabet's Google, LinkedIn, Lyft, Meta's Facebook, Microsoft, Netflix, Twitter, Uber, and Yandex have invested tremendous resources in online controlled experiments (OCEs) to assess the impact of innovation on their customers and businesses. Running OCEs at scale has presented a host of challenges requiring solutions from many domains. In this article we review challenges that require new statistical methodologies to address them. In particular, we discuss the practice and culture of online experimentation, as well as its statistics literature, placing the current methodologies within their relevant statistical lineages and providing illustrative examples of OCE applications. Our goal is to raise academic statisticians' awareness of these new research opportunities to increase collaboration between academia and the online industry. [ABSTRACT FROM AUTHOR]

The way of thinking of mathematicians and chemists in their respective disciplines seems to have very different levels of abstractions. While the firsts are involved in the most abstract of all sciences, the seconds are engaged in a practical, mainly experimental discipline. Therefore, it is surprising that many luminaries of the mathematics universe have studied chemistry as their main subject. Others have started studying chemistry before swapping to mathematics or have declared some admiration and even love for this discipline. Here I reveal some of these mathematicians who were involved in chemistry from a biographical perspective. Then, I analyze what these remarkable mathematicians and statisticians could have learned while studying chemical subjects. I found analogies between code-breaking and molecular structure elucidation, inspiration for statistics in quantitative analytical chemistry, and on the role of topology in the study of some organic molecules. I also analyze some parallelisms between the way of thinking of organic chemists and mathematicians in terms of the use of backward analysis, search for patterns, and use of pictures in their respective researches. [ABSTRACT FROM AUTHOR]

The present work proposes a new two-stage unrelated randomized response model to estimate the mean number of individuals who possess a rare sensitive attribute in a given population by using Poisson probability distribution, when the proportion of rare non-sensitive unrelated attribute is known and unknown. The properties of the proposed model are examined. The variance of the proposed randomized response model smaller than Land et al. (Stat J Theor Appl Stat, 46(3):351–360, 2012) and Singh and Tarray (Model Assist Stat Appl, 10(2):129–138, 2015) to estimate sensitive characteristic under study. The proposed model provides a more efficient unbiased estimator of the mean number of individuals. The procedure also introduces the measure of privacy protection of respondents and compares randomized response models in terms of efficiency and privacy protection. Empirical illustrations are presented to support the theoretical results and suitable recommendations are put forward to the survey statisticians/practitioners. [ABSTRACT FROM AUTHOR]

Post-stratification is applied when the subpopulation membership is observed only for sampled values and the goal is to estimate stratum-specific parameters which leads the survey statisticians towards primary goals i.e., classification of non-sampled units into different strata and prediction of the values of the study variables. Regression models, on one side, optimize the prediction of the study variable's non-sampled values while the classification algorithms, on the other side, look for the classification of non-sampled cases into different strata. Hence, it is crucial to deal with these two goals simultaneously for the estimation of stratum-specific parameters. This study introduces the idea of a double-objective classification and regression trees (CARTs) approach for estimating stratum-specific parameters. Theoretical properties of the total estimator are derived. An application on the estimation of health outcomes in different domains is given to delineate the practical significance as well as the efficiency of the proposed CART-based method. The proposed estimator of population total performs better than the existing stratum-specific estimator in terms of relative efficiency for all choices of parameters. As an ensemble model, the random forest CART outperforms the other competing tree-based models and homogenous population model without using any auxiliary variable. [ABSTRACT FROM AUTHOR]

Background: Statisticians are fundamental in ensuring clinical research, including clinical trials, are conducted with quality, transparency, reproducibility and integrity. Good Clinical Practice (GCP) is an international quality standard for the conduct of clinical trials research. Statisticians are required to undertake training on GCP but existing training is generic and, crucially, does not cover statistical activities. This results in statisticians undertaking training mostly unrelated to their role and variation in awareness and implementation of relevant regulatory requirements with regards to statistical conduct. The need for role-relevant training is recognised by the UK NHS Health Research Authority and the Medicines and Healthcare products Regulatory Agency (MHRA). Methods: The Good Statistical Practice (GCP for Statisticians) project was instigated by the UK Clinical Research Collaboration (UKCRC) Registered Clinical Trials Unit (CTU) Statisticians Operational Group and funded by the National Institute for Health and Care Research (NIHR), to develop materials to enable role-specific GCP training tailored to statisticians. Review of current GCP training was undertaken by survey. Development of training materials were based on MHRA GCP. Critical review and piloting was conducted with UKCRC CTU and NIHR researchers with comment from MHRA. Final review was conducted through the UKCRC CTU Statistics group. Results: The survey confirmed the need and desire for the development of dedicated GCP training for statisticians. An accessible, comprehensive, piloted training package was developed tailored to statisticians working in clinical research, particularly the clinical trials arena. The training materials cover legislation and guidance for best practice across all clinical trial processes with statistical involvement, including exercises and real-life scenarios to bridge the gap between theory and practice. Comprehensive feedback was incorporated. The training materials are freely available for national and international adoption. Conclusion: All research staff should have training in GCP yet the training undertaken by most academic statisticians does not cover activities related to their role. The Good Statistical Practice (GCP for Statisticians) project has developed and extensively piloted new, role-specific, comprehensive, accessible GCP training tailored to statisticians working in clinical research, particularly the clinical trials arena. This role-specific training will encourage best practice, leading to transparent and reproducible statistical activity, as required by regulatory authorities and funders. [ABSTRACT FROM AUTHOR]

Neural Bayes estimators are neural networks that approximate Bayes estimators. They are fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification. In this article, we aim to increase the awareness of statisticians to this relatively new inferential tool, and to facilitate its adoption by providing user-friendly open-source software. We also give attention to the ubiquitous problem of estimating parameters from replicated data, which we address using permutation-invariant neural networks. Through extensive simulation studies we demonstrate that neural Bayes estimators can be used to quickly estimate parameters in weakly identified and highly parameterized models with relative ease. We illustrate their applicability through an analysis of extreme sea-surface temperature in the Red Sea where, after training, we obtain parameter estimates and bootstrap-based confidence intervals from hundreds of spatial fields in a fraction of a second. [ABSTRACT FROM AUTHOR]

Background: In early 2021, the Department of Health and Social Care in the UK called for research on the safety and immunogenicity of concomitant administration of COVID-19 and influenza vaccines. Co-administration of these vaccines would facilitate uptake and reduce the number of healthcare visits required. The ComFluCOV trial was designed to deliver the necessary evidence in time to inform the autumn (September–November) 2021 vaccination policy. This paper presents the statistical methodology applied to help successfully deliver the trial results in 6 months. Methods: ComFluCOV was a parallel-group multicentre randomised controlled trial managed by the Bristol Trials Centre. Two study statisticians, supported by a senior statistician, worked together on all statistical tasks. Tools were developed to aid the pre-screening process. Automated data monitoring reports of clinic data and electronic diaries were produced daily and reviewed by the trial team and feedback provided to sites. Analyses were performed independently in parallel, and derivations and results of all outcomes were compared. Results: Set-up was achieved in less than a month, and 679 participants were recruited over 8 weeks. A total of 537 [at least] daily reports outlining recruitment, protocol adherence, and data quality, and 695 daily reports of participant electronic diaries identifying any missed diary entries and adverse events were produced over a period of 16 weeks. A preliminary primary outcome analysis of validated data was reported to the Department of Health and Social Care in May 2021. The database was locked 6 weeks after the final participant follow-up and final analyses completed 3 weeks later. A pre-print publication was submitted within 14 days of the results being made available. The results were reported 6 months after first discussions about the trial. Conclusion: The statistical methodologies implemented in ComFluCOV helped to deliver the study in the timescale set. Working in a new clinical area to tight timescales was challenging. Having two statisticians working together on the study provided a quality assurance process that enabled analyses to be completed efficiently and ensured data were interpreted correctly. Processes developed could be applied to other studies to maximise quality, reduce the risk of errors, and overall provide enhanced validation methods. Trial registration: ISRCTN14391248, registered on 30 March 2021 [ABSTRACT FROM AUTHOR]

Phenomenological control is the ability to alter experience according to goals, measured by responses to imaginative suggestion. The capacity to create vivid mental imagery has been thought to contribute to generation of experiences following suggestions during hypnosis, but evidence is mixed. Moreover, no study has yet investigated this link outside of hypnosis. Across two studies (n = 639 and n = 66), we tested the relationship between imagery vividness and both non-hypnotic and hypnotic imaginative suggestion scales. Our samples included, for the first time, people lacking voluntary imagery (aphantasics). We also assessed this relationship following commonly used approaches to studying aphantasia (direct recruitment of self-reported aphantasics), versus those designed to minimize demand characteristics effects. We observed a weak positive relationship between imagery vividness and phenomenological control, with aphantasics being slightly lower in this trait than non-aphantasics. However, this effect was exaggerated among self-reported aphantasics, who scored lower on phenomenological control than samples experiencing imagery as well as aphantasics assessed via single-blind procedure. Our results have been certified as computationally reproducible by an independent statistician. We conclude that, while imagery may contribute to responding to imaginative suggestions, it is crucial to follow single-blind recruitment to reduce the effect of demand characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Horizonte de la Ciencia is the property of Horizonte de la Ciencia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of PODIUM- Revista de Ciencia y Tecnologia en la Cultura Física is the property of Universidad de Pinar del Rio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Orthoses and prostheses were Chosen and laminated based on their high Yield, ultimate stresses, bending stresses, and fatigue limit. Response Surface Methodology (RSM) was utilized to find the best values for two parameters reinforcement perlon fiber and percent of Titanium Nanoparticle coupled with the matrix resin during optimization. The response surface methodology combined the expertise of mathematicians and statisticians to construct and analyze experimental models. Using this method, we identified 13 different lamination samples comprising a wide range of perlon number and Ti nano Wt% in their Perlon layer composition. All lamination materials defined by RSM methods and produced by a vacuum system were subjected to a battery of tests, with fatigue tests performed on the ideal laminating material in contrast to laminations created in the first study (Tensile test, Bending test, and Fatigue tests according to the ASTM D638 and D790 respectively). In comparison to the other 12 laminations tested using Design Expert version 10.0.2, the lamination with ten perlon layers and 0.75 percent Ti nano proved to be the strongest overall in terms of Yield, ultimate, and bending loads. This study used composite materials and titanium nanoparticles to characterize and fabricate ankle foot orthoses. Strength in bending should amount to about 70 MPa, around 85 MPa in tensile tension. Two empirical quadratic equations for the models of peak bending strength and maximum tensile stress with 95% confidence were created using the response surface approach and analysis of variance within the design of experiments software. [ABSTRACT FROM AUTHOR]

Background: Patient and public involvement (PPI) in trials aims to enhance research by improving its relevance and transparency. Planning for statistical analysis begins at the design stage of a trial within the protocol and is refined and detailed in a Statistical Analysis Plan (SAP). While PPI is common in design and protocol development it is less common within SAPs. This study aimed to reach consensus on the most important and relevant statistical analysis items within an SAP to involve patients and the public. Methods: We developed a UK-based, two-round Delphi survey through an iterative consultation with public partners, statisticians, and trialists. The consultation process started with 55 items from international guidance for statistical analysis plans. We aimed to recruit at least 20 participants per key stakeholder group for inclusion in the final analysis of the Delphi survey. Participants were asked to vote on each item using a Likert scale from 1 to 9, where a rating of 1 to 3 was labelled as having 'limited importance'; 4 to 6 as 'important but not critical' and 7 to 9 as 'critical' to involve patients and the public. Results from the second round determined consensus on critical items for PPI. Results: The consultation exercise led to the inclusion of 15 statistical items in the Delphi survey. We recruited 179 participants, of whom 72% (129: 36 statisticians, 29 patients or public partners, 25 clinical researchers or methodologists, 27 trial managers, and 12 PPI coordinators) completed both rounds. Participants were on average 48 years old, 60% were female, 84% were White, 64% were based in England and 84% had at least five years' experience in trials. Four items reached consensus regarding critical importance for patient and public involvement: presentation of results to trial participants; summary and presentation of harms; interpretation and presentation of findings in an academic setting; factors impacting how well a treatment works. No consensus was reached for the remaining 11 items. In general, the results were consistent across stakeholder groups. Discussion: We identified four critical items to involve patients and the public in statistical analysis plans. The remaining 11 items did not reach consensus and need to be considered in a case-by-case basis with most responders considering patient and public involvement important (but not critical). Our research provides a platform to enable focused future efforts to improve patient and public involvement in trials and enhance the relevance of statistical analyses to patients and the public. [ABSTRACT FROM AUTHOR]

We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating the Riemannian gradient descent and a novel gradient pruning procedure. Under suitable conditions, the algorithm converges linearly and can simultaneously estimate both the low-rank and sparse tensors. The statistical error bounds of final estimates are established in terms of the gradient of loss function. The error bounds are generally sharp under specific statistical models, for example, the sub-Gaussian robust PCA and Bernoulli tensor model. Moreover, our method achieves nontrivial error bounds for heavy-tailed tensor PCA whenever the noise has a finite 2 + ε moment. We apply our method to analyze the international trade flow dataset and the statistician hypergraph coauthorship network, both yielding new and interesting findings. for this article are available online. [ABSTRACT FROM AUTHOR]

When ethics requirements are confined to courses taught by non-STEM faculty, students may erroneously conclude that such considerations are at most marginal to their professional training. Primary source readings and discussion frameworks can tangibly link data analysis to questions of values within data science, computing ethics, or statistics courses. The particular cases of Sir Francis Galton and Sir Ronald Fisher--both statisticians and eugenicists--provide a rich collection of easily accessible digitized primary source material as well as a vast number of related popular and technical publications. Galton and Fisher figured centrally in the development of statistics as it is currently practiced (a substantial proportion of the technical content of introductory statistics courses were introduced by these men). However, the familiar tools of correlation and hypothesis testing were developed to analyze human data, often in service to the cause of eugenics. This article critically describes how we embedded problematic primary sources from Galton and Fisher into our courses and the discussions that ensued. We conclude with ideas for further implementations. [ABSTRACT FROM AUTHOR]

Abstract Recently, the Blinding of Trial Statisticians research team, Iflaifel and colleagues, have produced detailed guidance regarding the blinding or unblinding of statisticians in clinical trials, based on substantial mixed-methods work. I wish to comment on the research findings. In particular, I argue that open-label trials, non-drug trials, or non-inferiority trials should not be treated any differently from blinded superiority trials with regards to the risk of bias assessment. Prevention of bias should be the priority for definitive randomised controlled trials, regardless of the precise study design.

This article proposes a mobile learning procedure (m-Learning) in university students based on projects, which expresses as a scientific novelty the establishment of a path that links face-to-face and virtual teaching using mobile devices, through projects as a way of organizing the professional training process of university students. The research was oriented in a quantitative approach of pre-experimental type. The sample under study consisted of 100 students from the University of Holguín of the Mechanical Engineering career. They were used as methods documentary, systemic, observation analysis, the pedagogical pre-experiment, and the Chi-Square statistician (X2). After analyzing the data, it was found the existence of significant transformations in the learning of these students that generated favorable impacts in the productive and service processes in which they were inserted at work, concluding that this method from its external appearance and internal structure contributes to raising the quality of professional training for students in times of pandemic. [ABSTRACT FROM AUTHOR]

Copyright of Mendive - Revista de Educacion is the property of Universidad de Pinar del Rio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Of great importance in the economic activities of the zemstvos was statistical work on determining the socio-economic condition of peasant households, which were the basis of the country’s productive forces. Zemstvo statisticians independently developed and improved the methodology for conducting them. L. S. Lichkov, who was one of the first to join the ranks of Zemstvo statisticians, made his contribution to it. The paper attempts to analyze methods for obtaining primary data, methods for processing them, methods for grouping digital material that were proposed by him. His practical activities as the head of the Zemstvo statistical service of the Saratov gubernia are also considered. The author concludes that the methodology for collecting and processing statistical material, which he proposed and used in his work, was relatively effective and, in general, made it possible to obtain complete and reliable information about the peasant community. Heading the Saratov statistics, L. S. Lichkov brought it into the category of advanced ones.

Abstract Background Existing guidelines recommend statisticians remain blinded to treatment allocation prior to the final analysis and that any interim analyses should be conducted by a separate team from the one undertaking the final analysis. However, there remains substantial variation in practice between UK Clinical Trials Units (CTUs) when it comes to blinding statisticians. Therefore, the aim of this study was to develop guidance to advise CTUs on a risk-proportionate approach to blinding statisticians within clinical trials. Methods This study employed a mixed methods approach involving three stages: (I) a quantitative study using a cohort of 200 studies (from a major UK funder published between 2016 and 2020) to assess the impact of blinding statisticians on the proportion of trials reporting a statistically significant finding for the primary outcome(s); (II) a qualitative study using focus groups to determine the perspectives of key stakeholders on the practice of blinding trial statisticians; and (III) combining the results of stages I and II, along with a stakeholder meeting, to develop guidance for UK CTUs. Results After screening abstracts, 179 trials were included for review. The results of the primary analysis showed no evidence that involvement of an unblinded trial statistician was associated with the likelihood of statistically significant findings being reported, odds ratio (OR) 1.02 (95% confidence interval (CI) 0.49 to 2.13). Six focus groups were conducted, with 37 participants. The triangulation between stages I and II resulted in developing 40 provisional statements. These were rated independently by the stakeholder group prior to the meeting. Ten statements reached agreement with no agreement on 30 statements. At the meeting, various factors were identified that could influence the decision of blinding the statistician, including timing, study design, types of intervention and practicalities. Guidance including 21 recommendations/considerations was developed alongside a Risk Assessment Tool to provide CTUs with a framework for assessing the risks associated with blinding/not blinding statisticians and for identifying appropriate mitigation strategies. Conclusions This is the first study to develop a guidance document to enhance the understanding of blinding statisticians and to provide a framework for the decision-making process. The key finding was that the decision to blind statisticians should be based on the benefits and risks associated with a particular trial.

In recent years, the adjunct faculty phenomenon has grown steadily. This research focused on adjunct instructors teaching introductory statistics courses. The purpose of the study was to give a voice to adjunct instructors by allowing them to describe their experiences teaching statistics. We conducted a qualitative study with 15 adjunct instructors of introductory statistics through semi-structured interviews. The participants came from several fields:psychology, nursing, and business, among others. Thematic analysis was used to find themes of statistical anxiety, use of technology in the classroom, lack of curriculum flexibility, and connection to the host institution. Our findings can inform institutions and interdisciplinary departments hosting introductory statistics courses. Similarly, educational statisticians can gain knowledge on the practices and barriers of adjunct instructors. [ABSTRACT FROM AUTHOR]

This essay explores the material imperatives of nineteenth-century statistical ambition, at a time when the nature of paper itself underwent rapid change. It expands the concept of paper technologies to include knowledge about the materials themselves, as well as the infrastructures, techniques, and agreements that made it possible for the right kind of paper to be made available for particular purposes. Taking the production of the forms for the Prussian census as a case in point, it argues that the enumeration effort of Prussian census statisticians went far beyond designing a form, collecting information, noting and compiling figures, or creating tables. Paper knowledge to define and control the material substance of the forms mattered as much as political impetus and statistical methods in getting the census right. [ABSTRACT FROM AUTHOR]

Microbiological data are used as indicators of infection, for diagnosis, and the identification of antimicrobial resistance in trials of antimicrobial stewardship interventions. However, several problems have been identified in a recently conducted systematic review (e.g., inconsistency in reporting and oversimplified outcomes), which motivates the need to understand and improve the use of these data including analysis and reporting. We engaged key stakeholders including statisticians, clinicians from both primary and secondary care, and microbiologists. Discussions included issues identified in the systematic review and questions about the value of using microbiological data in clinical trials, perspectives on current microbiological outcomes reported in trials, and alternative statistical approaches to analyse these data. Various factors (such as unclear sample collection process, dichotomising or categorising complex microbiological data, and unclear methods of handling missing data) were identified that contributed to the low quality of the microbiological outcomes and the analysis of these outcomes in trials. Whilst not all of these factors would be easy to overcome, there is room for improvement and a need to encourage researchers to understand the impact of misusing these data. This paper discusses the experience and challenges of using microbiological outcomes in clinical trials. [ABSTRACT FROM AUTHOR]

Presenters in the Surgical and Allied session included Mr. Ahmad Sohail, Dr. Asifa Malhan, Dr. Hamra Afridi, DrAnum Asif, Dr. Rafia Wakil, Dr. Iqra Jam, Dr. Erum Majid, Dr. M. Daniyal, Dr. Zubair Ahmed, Dr. Ayesha Iqbal and Dr. Kauser Nazeer. Those selected for oral presentations in the Medicine and Allied Session included Dr. Hania, Dr. Saba Asghar, Dr. Lajpat, Dr. Urooj, Dr. Ghulam Muhiuddin, Dr. Bushra Shahid, Dr. Chanchal Kumari, Dr. Gulzar Khan, Dr. Kanwal Buttani, Dr. Momna Mazhar and Dr. Saima Mushtaq. There were ten presentations in the Basic Medical Sciences and the presenters included Dr. Unaiza Pervez, Dr. Shaimuna Fareeha, Dr. Qurat ul Ain, Dr. Sarah Zahid, Dr. Kausar Abbas, Dr. Mehreen Amer, Dr. Komal Moorpani, Dr. Waqas Nisar, Dr. Naila and Dr. Iqra Jam. [Extracted from the article]

Recently, the Blinding of Trial Statisticians research team, Iflaifel and colleagues, have produced detailed guidance regarding the blinding or unblinding of statisticians in clinical trials, based on substantial mixed-methods work. I wish to comment on the research findings. In particular, I argue that open-label trials, non-drug trials, or non-inferiority trials should not be treated any differently from blinded superiority trials with regards to the risk of bias assessment. Prevention of bias should be the priority for definitive randomised controlled trials, regardless of the precise study design. [ABSTRACT FROM AUTHOR]

This article discusses the importance of statistical analysis in medical research and explores the current state of statistical analysis in research papers published in the Indian Journal of Anaesthesia (IJA). The study found that collaboration with statisticians is widespread among authors, with 36% involving the statistical department of their institutes and 40% engaging private statisticians. However, a significant number of authors (72%) lacked formal training in statistics, highlighting the need for enhanced statistical education in medical research. The study also revealed varying levels of confidence among authors in interpreting and communicating statistical results, emphasizing the importance of targeted training and support. The article concludes that effective collaboration between authors and statisticians can lead to more robust research outcomes and higher-quality publications. [Extracted from the article]

Statisticians work with figures, whereas scientists work with estimated quantities. Every direct (physical) measurement has some degree of uncertainty. Single numbers pose no problems, and an implied range can always be specified. Difficulties arise when those numbers or sets of numbers are used to calculate derived figures. Statistical measures such as ratios can be skewed if uncertainty about the actual measurements used to derive those quantities is not taken into account. This lack of consideration may lead to incorrect figures being used and calls into question the criteria used to diagnose, identify or delimit new species. In this case study, I use data gathered from the literature on different species of the clade Hydrachnidia (Acari, Parasitengona) to show how range ratios of important characters differ when uncertainty is considered. I outline the successive steps taken during the measuring process—from microscope calibration to the calculation of several statistical values from the direct measurements—and suggest some corrections. I anticipate that the results and recommendations presented here will be applicable to other taxonomic groups for which linear measurements play a central role in the description and identification of species. [ABSTRACT FROM AUTHOR]

In the context of a sample survey, the collection of information on a sensitive variable is difficult, which may cause nonresponse and measurement errors. Due to this, the estimates can be biased and the variation may increase. To overcome this difficulty, we propose an estimator for the estimation of a sensitive variable by using auxiliary information in the presence of nonresponse and measurement errors simultaneously. The properties of the proposed estimators have been studied, and the results have been compared with those of the usual complete response estimator. Theoretical results have been verified through a simulation study using an artificial population and two real-life applications. With the outcomes of the proposed estimator, a suitable recommendation has been made to the survey statisticians for their real-life application. [ABSTRACT FROM AUTHOR]

Background: Existing guidelines recommend statisticians remain blinded to treatment allocation prior to the final analysis and that any interim analyses should be conducted by a separate team from the one undertaking the final analysis. However, there remains substantial variation in practice between UK Clinical Trials Units (CTUs) when it comes to blinding statisticians. Therefore, the aim of this study was to develop guidance to advise CTUs on a risk-proportionate approach to blinding statisticians within clinical trials. Methods: This study employed a mixed methods approach involving three stages: (I) a quantitative study using a cohort of 200 studies (from a major UK funder published between 2016 and 2020) to assess the impact of blinding statisticians on the proportion of trials reporting a statistically significant finding for the primary outcome(s); (II) a qualitative study using focus groups to determine the perspectives of key stakeholders on the practice of blinding trial statisticians; and (III) combining the results of stages I and II, along with a stakeholder meeting, to develop guidance for UK CTUs. Results: After screening abstracts, 179 trials were included for review. The results of the primary analysis showed no evidence that involvement of an unblinded trial statistician was associated with the likelihood of statistically significant findings being reported, odds ratio (OR) 1.02 (95% confidence interval (CI) 0.49 to 2.13). Six focus groups were conducted, with 37 participants. The triangulation between stages I and II resulted in developing 40 provisional statements. These were rated independently by the stakeholder group prior to the meeting. Ten statements reached agreement with no agreement on 30 statements. At the meeting, various factors were identified that could influence the decision of blinding the statistician, including timing, study design, types of intervention and practicalities. Guidance including 21 recommendations/considerations was developed alongside a Risk Assessment Tool to provide CTUs with a framework for assessing the risks associated with blinding/not blinding statisticians and for identifying appropriate mitigation strategies. Conclusions: This is the first study to develop a guidance document to enhance the understanding of blinding statisticians and to provide a framework for the decision-making process. The key finding was that the decision to blind statisticians should be based on the benefits and risks associated with a particular trial. [ABSTRACT FROM AUTHOR]

This paper shows that dropout training in generalized linear models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's covariates using a multiplicative nonparametric errors-in-variables model. In this game, nature's least favorable distribution is dropout noise, where nature independently deletes entries of the covariate vector with some fixed probability δ. This result implies that dropout training indeed provides out-of-sample expected loss guarantees for distributions that arise from multiplicative perturbations of in-sample data. The paper makes a concrete recommendation on how to select the tuning parameter δ. The paper also provides a novel, parallelizable, unbiased multi-level Monte Carlo algorithm to speed-up the implementation of dropout training. Our algorithm has a much smaller computational cost compared to the naive implementation of dropout, provided the number of data points is much smaller than the dimension of the covariate vector. [ABSTRACT FROM AUTHOR]

The paper argues that (i) the notion of epistemic reliability, as it is standardly defined within mainstream epistemology, is a multidimensional concept, and that (ii) paying attention to reliability's multidimensional nature can significantly expand reliabilism's purview both in the theoretical and practical domain. Reliabilist theories of knowledge and justification agree that a process is reliable just in case it leads to a 'sufficiently high preponderance of true over false beliefs.' Given this straightforward definition, reliability appears to be, and so far has been implicitly treated by epistemologists as, a mono-dimensional concept. Nevertheless, the terminology and formulas that statisticians and computers scientists have developed for assessing the reliability of binary classification processes reveal that for any belief-forming process whose aim is to identify the presence or the absence of some quality ι, there are at least four different interpretations of the above definition. These interpretations correspond to four separate dimensions of reliability: Precision, Negative Predictive Value (NPV), Recall and Specificity. Though epistemologists do not explicitly specify how they interpret the above definition of reliability, due to their main interest in 'know-that', their mono-dimensional focus seems to have so far been mainly directed at Precision alone (or, at best, to both Precision and NPV—but without distinguishing between the two). As it transpires, however, the (entirely) neglected dimensions of Recall and Specificity can guide the satisfaction of another important epistemic goal, best captured by the locution 'know-most/all.' The upshot is that different epistemic goals call for high levels of reliability along different dimensions (and, often, their combinations too). This observation invites upgrading reliabilism into dimensional reliabilism, according to which assessing whether a process is knowledge-conducive depends on whether it manifests high reliability along the appropriate dimension(s), given the epistemic goal at hand. [ABSTRACT FROM AUTHOR]

Recently, Citro et al. published an article focusing on the autonomy, or lack of same, of the 13 principle statistical agencies of the United States. The authors are to be congratulated for raising an important topic—the concept of autonomy. Among their conclusions are: (a) existing autonomy protections are inadequate, (b) a lack of professional autonomy unduly exposes the principal federal statistical agencies to efforts to undermine the objectivity of their products and (c) agencies cannot completely rebuff these efforts. Their main recommendations are that the role of the Chief Statistician be strengthened and new statutory autonomy protections be legislated. Here, we consider the meaning of autonomy for a federal agency in general and for federal statistical agencies in particular. Additionally, we consider the benefits and limitations of autonomy for federal statistical agencies. We note that while additional legislation is useful to produce required autonomy, a powerful tool—and one which is possibly more readily available—is effective leadership. Finally, we suggest that the process used to select the leaders of the statistical system needs to be fundamentally changed. [ABSTRACT FROM AUTHOR]

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Hidden Markov models (HMMs) and their extensions are attractive methods for analysing ecological data where noisy, multivariate measurements are made of a hidden, ecological process, and where this hidden process is represented by a sequence of discrete states. Yet, as these models become more complex and challenging to understand, it is important to consider what pitfalls these methods have and what opportunities there are for future research to address these pitfalls.In this paper, we review five lesser known pitfalls one can encounter when using HMMs or their extensions to solve ecological problems: (a) violation of the snapshot property in continuous‐time HMMs; (b) biased inference from hierarchical HMMs when applied to temporally misaligned processes; (c) sensitive inference from using random effects to partially pool across heterogeneous individuals; (d) computational burden when using HMMs to approximate models with continuous state spaces; and (e) difficulty linking the hidden process to space or environment.This review is for ecologists and ecological statisticians familiar with HMMs, but who may be less aware of the problems that arise in more specialised applications. We demonstrate how each pitfall arises, by simulation or example, and discuss why this pitfall is important to consider. Along with identifying the problems, we highlight potential research opportunities and offer ideas that may help alleviate these pitfalls.Each of the methods we review are solutions to current ecological research problems. We intend for this paper to heighten awareness of the pitfalls ecologists may encounter when applying these more advanced methods, but we also hope that by highlighting future research opportunities, we can inspire ecological statisticians to weaken these pitfalls and provide improved methods. [ABSTRACT FROM AUTHOR]

Abstract Background Blinding is an established approach in clinical trials which aims to minimise the risk of performance and detection bias. There is little empirical evidence to guide UK clinical trials units (CTUs) about the practice of blinding statisticians. Guidelines recommend that statisticians remain blinded to allocation prior to the final analysis. As these guidelines are not based on empirical evidence, this study undertook a qualitative investigation relating to when and how statisticians should be blinded in clinical trials. Methods Data were collected through online focus groups with various stakeholders who work in the delivery and oversight of clinical trials. Recordings of the focus groups were transcribed verbatim and thematic analysis was used to analyse the transcripts. Results Thirty-seven participants from 19 CTUs participated in one of six focus groups. Four main themes were identified, namely statistical models of work, factors affecting the decision to blind statisticians, benefits of blinding/not blinding statisticians and practicalities. Factors influencing the decision to blind the statistician included available resources, study design and types of intervention and outcomes and analysis. Although blinding of the statistician is perceived as a desirable mitigation against bias, there was uncertainty about the extent to which an unblinded statistician might impart bias. Instead, in most cases, the insight that the statistician offers was deemed more important to delivery of a trial than the risk of bias they may introduce if unblinded. Blinding of statisticians was only considered achievable with the appropriate resource and staffing, which were not always available. In many cases, a standard approach to blinding was therefore considered unrealistic and impractical; hence the need for a proportionate risk assessment approach identifying possible mitigations. Conclusions There was wide variation in practice between UK CTUs regarding the blinding of trial statisticians. A risk assessment approach would enable CTUs to identify risks associated with unblinded statisticians conducting the final analysis and alternative mitigation strategies. The findings of this study will be used to design guidance and a tool to support this risk assessment process.

Abstract Background At the 2015 REWARD/EQUATOR conference on research waste, the late Doug Altman revealed that his only regret about his 1994 BMJ paper ‘The scandal of poor medical research’ was that he used the word ‘poor’ rather than ‘bad’. But how much research is bad? And what would improve things? Main text We focus on randomised trials and look at scale, participants and cost. We randomly selected up to two quantitative intervention reviews published by all clinical Cochrane Review Groups between May 2020 and April 2021. Data including the risk of bias, number of participants, intervention type and country were extracted for all trials included in selected reviews. High risk of bias trials was classed as bad. The cost of high risk of bias trials was estimated using published estimates of trial cost per participant. We identified 96 reviews authored by 546 reviewers from 49 clinical Cochrane Review Groups that included 1659 trials done in 84 countries. Of the 1640 trials providing risk of bias information, 1013 (62%) were high risk of bias (bad), 494 (30%) unclear and 133 (8%) low risk of bias. Bad trials were spread across all clinical areas and all countries. Well over 220,000 participants (or 56% of all participants) were in bad trials. The low estimate of the cost of bad trials was £726 million; our high estimate was over £8 billion. We have five recommendations: trials should be neither funded (1) nor given ethical approval (2) unless they have a statistician and methodologist; trialists should use a risk of bias tool at design (3); more statisticians and methodologists should be trained and supported (4); there should be more funding into applied methodology research and infrastructure (5). Conclusions Most randomised trials are bad and most trial participants will be in one. The research community has tolerated this for decades. This has to stop: we need to put rigour and methodology where it belongs — at the centre of our science.

A new study shows that carrion crows use memorized reward associations in a combinatorial way to apply relative probabilistic information to optimize reward outcome. This for the first time shows that a corvid species can flexibly apply statistical inference during decision making. [ABSTRACT FROM AUTHOR]

Forecasting for time series has always been of interest to statisticians and data scientists because it offers a lot of benefits in reality. This study proposes the fuzzy time series model which can both interpolate historical data, and forecast effectively for the future with the important contributions. First, we build the universal set based on the percentage of the original data variation, and divide it to clusters with the suitable number by the developed automatic algorithm. Next, the new fuzzy relationship between each element in series and the obtained clusters is established. The bigger the variation is, the more the clusters are divided. Finally, combining the two above improvements, we propose the new principle to forecast for the future. The experiments on many well-known data sets, including 3003 series of M3-competition data show that the proposed model has shown the outstanding advantage in comparing to the existing ones. Because the proposed model is established by the Matlab procedure, it can apply effectively for real series. [ABSTRACT FROM AUTHOR]

This article reconstructs the anthropological debate on the Southern question, focusing on the decade between 1897 and 1907 and on three protagonists: the statistician and criminologist Alfredo Niceforo, the economist and politician Napoleone Colajanni and the anthropologist and psychologist Giuseppe Sergi. Sergi's theories on the Mediterranean „race", its African origin and role in the population and above all in the process of civilization of the European continent were used to support opposing positions: Niceforo referred to them to affirm the backwardness of the population of Southern Italy; Colajanni, by contrast, employed them to defend the dignity of the inhabitants of the South, against the advocates of the superiority and inferiority of different „races". The analysis of these discussions shows how anthropologists and ethnologists, playing on the delicate balance between „race" and „environment", contributed – in often contradictory ways – to the socio-political project of defining Italian national identity in the early decades after Unification. [ABSTRACT FROM AUTHOR]

Best linear unbiased estimators (BLUE's) are known to be optimal in many respects under normal assumptions. Since variance minimization doesn't depend on normality and unbiasedness is often considered reasonable, many statisticians have felt that BLUE's ought to preform relatively well in some generality. The result here considers the general linear model and shows that any measurable estimator that is unbiased over a moderately large family of distributions must be linear. Thus, imposing unbiasedness cannot offer any improvement over imposing linearity. The problem was suggested by Hansen, who showed that any estimator unbiased for nearly all error distributions (with finite covariance) must have a variance no smaller than that of the best linear estimator in some parametric subfamily. Specifically, the hypothesis of linearity can be dropped from the classical Gauss–Markov Theorem. This might suggest that the best unbiased estimator should provide superior performance, but the result here shows that the best unbiased regression estimator can be no better than the best linear estimator. [ABSTRACT FROM AUTHOR]