Generative artificial intelligence (AI) technologies are transforming the world of education, but their impact on students’ thinking and learning remains unclear. In this study, we investigated AI’s potential role in supporting statistical thinking by interviewing undergraduate students ( N = 5 ) as they completed a graphing task using Rtutor.AI, an AI-powered tool that integrates ChatGPT with R. The analysis yielded five key themes that describe students’ statistical thinking while using Rtutor.AI. The first theme demonstrated how the iterative and intuitive nature of prompting within Rtutor.AI shaped participants’ approaches to problem-solving. The next three themes—“Building statistical understanding through a step-by-step process,” “Identifying key elements of a problem to create specific prompts,” and “Lowering barriers to completion of statistical tasks”—illustrated how Rtutor.AI facilitated statistical thinking in various ways. The fifth theme showed that students’ prior statistical knowledge influenced their ability to interpret and contextualize Rtutor.AI’s output, and that Rtutor.AI did not fully absolve students of the need to think statistically. Overall, these results highlight both the potential benefits and risks of incorporating AI into statistics classrooms and can serve as an empirical basis for future scholarship aimed at creating scaffolding around AI technologies to support statistics instruction.

This article examines how students can engage with generative artificial intelligence (genAI) as collaborators in the statistics learning process. Prompt engineering is positioned as a transferable, tool-agnostic competency that reinforces core elements of statistical thinking, including clarity, iteration, and purposeful inquiry. Through illustrative collaborations, we explore applications such as automating and optimizing code, acquiring programming syntax, and designing simulation studies. While these tasks are drawn from upper-level undergraduate and graduate coursework, the running example–a chi-squared test of association–is intended to spur ideas for incorporating genAI into the introductory statistics classroom. Supplementary materials include a) an outline of a learning management module and structure of the discussion and activities during my class periods covering this module on responsible use of generative AI; b) R Markdown files and compiled pdf documents intended to support classroom integration; c) illustrative comparisons across three widely used platforms–ChatGPT, Copilot, and Gemini–to highlight how differences in output style and reasoning can inform instructional design, rather than to rank or evaluate tools technically. The article concludes with a discussion of strategies for promoting ethical, transparent, and inclusive uses of genAI in statistics education.

When assessing the role and place of statistics in modern digital society, especially from the standpoint of the requirements for the training of analysts working in a wide variety of spheres of social activity [1], it is difficult to ignore a fairly wide range of discussion issues, among which possible approaches to understanding and quantifying statistical thinking are clearly distinguished. This article invites readers to familiarize themselves and discuss the concepts of “statistical” thinking, its meaning in the modern world.

Statistical thinking is a fundamental competency in mathematics education, particularly in enabling students to interpret, analyze, and make decisions based on data. One essential topic that supports the development of statistical thinking is data dispersion, which includes measures such as range, variance, standard deviation, and interquartile range. However, many elementary students experience difficulties in understanding these concepts due to abstract explanations and teacher-centered instructional practices. This study aims to enhance students’ statistical thinking skills through the implementation of cooperative learning in mathematics instruction focusing on data dispersion. The research employed Classroom Action Research (CAR) conducted in 2025 with fourth-grade students at a public elementary school in Indonesia. The study was implemented across three cycles, each consisting of planning, action, observation, and reflection stages. Data were collected through statistical thinking tests, observation sheets, and learning achievement assessments. The findings reveal a significant improvement in students’ statistical thinking abilities and learning outcomes across cycles, with classical mastery increasing from 61.1% in Cycle I to 83.3% in Cycle III. The results indicate that cooperative mathematics learning effectively facilitates conceptual understanding of data dispersion and fosters active engagement, reasoning, and collaboration among students. This study contributes to mathematics education research by providing empirical evidence on the role of cooperative learning in strengthening statistical thinking at the elementary level.

Modern scientific research is facing a severe crisis of reproducibility, with numerous published findings failing to replicate in subsequent independent verification, severely undermining the reliability of scientific knowledge. This paper systematically argues that the methodological imbalance between statistical significance and practical significance constitutes the core root cause of this crisis. We delve into how the misuse of p-values in current research practices—through p-value manipulation and selective reporting—spawns fragile findings. We also reveal the deep-seated impacts of academic incentive structures, publication bias, cognitive biases, and insufficient scientific literacy. Building on this analysis, we propose multidimensional solutions: at the research culture level, reforming incentive mechanisms to prioritize replicable studies and open science practices; and at the educational level, strengthening the cultivation of statistical thinking. The scientific community must achieve a paradigm shift from pursuing statistical significance to evaluating evidence strength and practical relevance, thereby building a more resilient and credible research ecosystem.

With the rapid advancement of artificial intelligence technology, traditional applied statistics education in higher education institutions faces the challenge of cultivating students' ability to solve complex real-world problems. Addressing the highly practical nature of the applied statistics discipline, this study explores the deep integration of the 5E teaching model with case-based teaching methods, empowered by AI technology. The paper first analyzes the inherent compatibility between the 5E teaching model and case-based teaching, then systematically constructs an "5E-C-AI" integration mechanism model under AI empowerment, and elaborates on specific implementation pathways within applied statistics instruction. Research demonstrates that this integrated model effectively stimulates students' inquiry interest, deepens statistical thinking, and enhances their comprehensive abilities in data acquisition, processing, modeling, and interpretation, thereby providing a new paradigm for cultivating innovative and application-oriented statistics professionals.

This study addresses the challenge of enhancing statistical thinking among prospective mathematics students’ through the development of interactive digital modules delivered via Android applications. Traditional learning resources in descriptive statistics often emphasize computational procedures at the expense of conceptual understanding and reasoning. Guided by the ADDIE (Analysis, Design, Develop, Implement, Evaluate) model within a research and development framework, this study designed and evaluated a digital module integrating learning activities, multimedia explanations, practice videos, exercises, and formative assessments. Validation by subject matter, media, and pedagogical experts indicated high levels of validity (0.90–0.91). Pilot and field trials demonstrated strong practicality (74.73% and 82.67%), while a paired sample t-test and N-Gain analysis confirmed significant improvements in students’ statistical thinking skills, with effectiveness reaching 80.36%. These findings suggest that Android-based modules not only provide valid and practical learning resources but also deliver measurable learning gains. The study contributes to the growing body of research on mobile and technology-enhanced learning by highlighting the pedagogical potential of interactive digital modules in higher education, particularly in bridging the gap between procedural knowledge and conceptual reasoning in statistics education.

Understanding the independence of random variables is essential, not just as a statistical skill, but as a way to nurture critical thinking and analytical reasoning, especially for future educators. This article explores the approaches taken by mathematics education students in Indonesia when faced with the task of testing for independence. Some students thoroughly examine all possible scenarios, while others focus on a single case, often unaware of the risks of overgeneralization. Through real classroom observations and student interviews, we reveal how these different strategies reflect not only the technical abilities of students but also their growth in mathematical maturity. By fostering a deeper, more comprehensive grasp of independence, we hope to support the development of future teachers who can guide the next generation in thoughtful and informed statistical thinking.

This article examines the evolution of special needs education in Switzerland, focusing on the transition from segregation to inclusion within the context of welfare state formation. The authors hypothesise that both segregated and inclusive education systems are inextricably linked to the logic of the Swiss welfare state, which aims to integrate individuals into society while reducing the financial burden on the state. Historical analysis reveals that, from the end of the nineteenth century, early welfare measures, driven by statistical thinking and medico-educational classifications, led to the establishment of special educational facilities for “abnormal” children. In the twentieth century, it became apparent that educational segregation led to social separation, not integration. However, despite the political commitment to the new paradigm of inclusive education, its practical implementation remains challenging, with significant variations among Swiss cantons and ongoing debates about resource allocation and meritocracy.

Reacting to the Past is a game-based pedagogy in which students take on roles in a broader historical conflict with elements of game fiction, competition, and collaboration. This paper introduces two Reacting to the Past games developed for introductory statistics courses: “The Cigarette Century”: Tobacco and Lung Cancer, 1964-1965 and Cholera! at the Pump: Contagionism, Miasma Theory and Sanitation, London 1854. Both games are used to teach specific statistical content including measures of risk, data visualization, and hypothesis testing while also using historical context and real datasets to emphasize statistical thinking and provide relevance. Reacting to the Past games as high impact practices are strongly in alignment with the 2016 GAISE recommendations including conceptual understanding, use of real data, and active learning. Both games were implemented in an “Applied Statistics for the Biological Sciences” course at a public four-year university. Students enrolled in the course (N = 115 over five semesters from Spring 2023 to Spring 2025) were primarily juniors or seniors (63%) and Biology or Human Health Science majors (75%). Assessment of performance in the games is based on portfolios that include scaffolded background research, posters presentations, labs, and reflections. Course evaluations show students generally found the games to be fun, interactive, and effective at teaching statistical thinking, although some students struggled with historical language, workload, and complexity of the projects. Both the Cigarette Century and Cholera 1854 games and additional games are available through the Reacting Consortium by instructor subscription ($25-$150 annually, income-based). The student-facing game books discussed in this paper are reproduced in the Supplementary Materials with permission from the authors and publisher. In addition, full game materials for Cigarette Century and Cholera 1854 are available at no cost on the Reacting website for one month following the publication of this article in a Journal’s Issue.

Artificial intelligence (AI) is rapidly transforming healthcare and medical education. Strong statistical thinking skills are vital for evaluating and applying AI tools. However, traditional medical statistics education has not adapted to this demand. This paper first analyzes the connotation and importance of statistical thinking, points out the significant challenges currently faced by medical statistics education, and then proposes strategies such as innovative teaching methods combined with evidence-based medicine, utilizing AI platforms for supplemental teaching, multidisciplinary integration, and strengthening the understanding of the statistical foundations of AI to enhance the statistical thinking abilities of medical professionals. This study emphasizes the importance of cultivating medical statistical thinking in the era of AI to improve the quality of medical education and ensure the safety and effectiveness of future medical services.

INTRODUCTION: Contemporary engineering operates in a data-rich yet uncertainty-laden landscape, particularly under the technological shifts introduced by Industry 4.0. While foundational, deterministic models frequently fail to address the ambiguity, variability, and incompleteness inherent in real-world data, this paper examines the growing need to embed statistical reasoning within digital engineering decision-making processes to ensure robustness and interpretability.OBJECTIVES: The study aims to investigate how statistical thinking contributes to innovation, transparency, and adaptive decision-making in digitalized engineering systems. It identifies conceptual gaps and underexplored themes in current literature and emphasizes the strategic relevance of probabilistic reasoning in addressing uncertainty across complex industrial settings.METHODS: A hybrid scoping review methodology was applied, combining a semantic AI-driven search via Elicit with a structured bibliographic query in Scopus. The resulting corpus of 928 curated publications was analysed through bibliometric techniques and social network analysis using VOSviewer. This comprehensive process enabled the identification of co-occurrence patterns, thematic clusters, and evolving disciplinary linkages, ensuring the credibility and reliability of the findings.RESULTS: Five primary research clusters emerged: decision optimization, risk management and human factors, machine learning integration, digital information systems, and sustainability. These clusters represent key areas where probabilistic modelling and uncertainty quantification can significantly enhance engineering practices. Although AI and big data analytics are increasingly prevalent, the underrepresentation of probabilistic modelling and uncertainty quantification in these clusters reveals a disconnect between data-centric innovation and risk-aware engineering practice.CONCLUSION: A conceptual shift toward probabilistic reasoning is advocated as a necessary response to the complexity of modern digital engineering environments. Repositioning statistical thinking as a central enabler of digital transformation supports the development of resilient, interpretable, and future-ready engineering systems. Integrating these methodologies into engineering curricula, AI pipelines, and industrial decision-support infrastructures is essential for advancing strategic, uncertainty-aware innovation.

The study explores the magnitude of Analyzing data Statistical thinking (ADST), Statistics Achievement (SA), and relationship between ADST and sub-factors of SA with ADST. 360 Undergraduate were selected from the population. Multi multistage stratified random sampling technique was used in the selection of samples. ADST test was used for the purpose of data collection. Mean, standard deviation and Pearson correlation coefficient were used to analyze the data. Means were compared with the t test statistic, and ANOVA was used for the interaction effect of gender and school sector. From the results of the study, it was concluded that SA has a statistically significant and strong Positive correlation with ADST and their sub factors. Gender-wise statistically significant differences exist in the means score of ADST and SA. The university sector-wise statistically significant differences do not exist in the means score of ADST and SA at the 0.05 level of significance. Gender and sector-wise interaction effect on ADST and SA is statistically significance does not exist on the means score.

Abstract The modern world is increasingly driven by data, making data literacy and data science competencies essential skills for informed citizenship and future career success. Traditional K-12 mathematics curricula, while foundational, often place a limited emphasis on authentic data investigation and the use of modern computational tools, primarily focusing on procedural processes. This research proposes an investigation into the effective integration of core data science practices and data literacy competencies into the existing K-12 mathematics curriculum. The study will explore various models for integration, from embedding data-rich, real-world projects into existing statistics and probability units to creating a coherent, spiralling framework across all grade levels. Key areas of focus include identifying essential mathematical and data literacy competencies, developing effective professional development for teachers, evaluating the impact on student critical thinking and problem-solving skills, and addressing ethical considerations related to data. The ultimate goal is to provide a comprehensive framework and evidence- based recommendations for curriculum reform that leverages data science to enhance the relevance, engagement, and equity of mathematics education. Keywords Data Science, Data Literacy, K-12 Education, Mathematics Curriculum, Curriculum Integration, Statistical Thinking, Computational Thinking, Professional Development, Equity in Education.

ABSTRACTThe paper describes six examples of poor statistical practice in public policy. The first example is the lack of a COVID Information Plan for Australia resulting in deficient information being used to understand the progress of the pandemic and the best public policy responses. The second example is inappropriate criteria being used for determining when to ease COVID restrictions as vaccination rates increased because they ignored the impact of uncertainty in the modelling assumptions. The third example is the machine learning algorithms used in Robodebt, which were flawed, used inappropriate data and did not incorporate measures of uncertainty. The fourth example is the opinion polls used in the 2019 Australian election, which got the result wrong because they relied on unrepresentative samples with inadequate weighting adjustments for this deficiency. The fifth example is from the United States where the salaries of teachers (and even their continued employment) were based on the performance of their students using regression models that were inadequate for the purpose. The sixth and more positive example is the use of purchasing power parities to influence two different global debates on poverty reduction and climate change. The paper concludes with suggestions on what the Australian statistical profession should do to address the lack of statistical thinking in many policy areas.

Ronald Davis Snee was born on December 11, 1941 in Washington PA, near Pittsburgh. He grew up on a farm and raised price-winning livestock. He was educated in a one-room schoolhouse before attending Washington and Jefferson College, obtaining a B.S. in mathematics, as well as finishing his wrestling season undefeated. He obtained a Ph.D. in statistics at Rutgers University, learning from Ellis Ott and Horace Andrews, among others. Ron joined the Applied Statistics Group at DuPont, eventually spending twenty-three years with the company in both technical and managerial roles. As a consultant, Ron has helped over 120 clients enhance quality, productivity, and especially profitability, across a wide range of organizations. Technically, he has made seminal contributions to mixture design and analysis, model validation, graphics and visualization, Six Sigma, as well as quality improvement in general. Perhaps more than anyone else, he developed and popularized the concept of statistical thinking, and more recently was instrumental in elucidating and popularizing statistical engineering. He has a long list of publications and awards, and has stayed professionally active into his 80’s.

This study presents a hybrid systematic literature review (SLR) and bibliometric analysis aimed at examining the development, trends, and theoretical implications of statistical literacy research. Using the PRISMA protocol, 89 relevant articles published between 2002 and 2025 were analysed to address three research questions concerning the significance of statistical literacy as a future research domain, the distribution of scholarly investigations, and their theoretical and practical implications. The findings show a consistent increase in research interest over the past decade, especially after 2017, with dominant themes including statistical literacy, statistics education, and data literacy. The analysis also reveals an imbalance in research allocation, with higher education receiving disproportionate attention compared to other educational levels. Moreover, the results underscore the integrative role of statistical thinking and reasoning as essential components of statistical literacy, with growing emphasis on digital learning, real-world data contexts, and interdisciplinary approaches. This review highlights both the progress and the existing gaps in the field, providing a roadmap for future research that prioritizes inclusive, context-sensitive, and pedagogically sound models for enhancing statistical literacy in a data-driven society. In addition to thematic and theoretical insights, this study also explores the most influential journals, authors, and citation networks, providing a clearer picture of the academic landscape surrounding statistical literacy. The bibliometric data help reveal publication patterns, collaboration trends, and emerging research clusters. These findings are valuable for identifying research gaps, informing curriculum development, and fostering cross-institutional and interdisciplinary partnerships. By highlighting these dimensions, the review contributes to a deeper understanding of how the field has evolved and where it is heading.

Purpose This paper attempts to explore the critical success factors (CSFs) of a green-lean-six sigma (GLSS) strategy to address the pressing environmental sustainability issues of the flexible packaging (FP) industry, including greenhouse gas emissions, resource depletion, and environmental degradation. Design/methodology/approach This study uses a multiple case study approach to explore the key factors behind the effective implementation of the GLSS strategy in the FP industry of New Zealand (NZ) and Pakistan (PK) and compare these in a developed (NZ) and developing (PK) economy context. In this perspective, primary and secondary data sources were used for data collection, including the semi-structured interviews with the senior corporate managers and organizations’ strategic reports, web pages, and sustainability reports. Findings Drawing on the intellectual capital-based view (ICBV), the findings revealed various organization-related, employee-related, and stakeholder-related factors that are crucial for the adequate utilization of the GLSS strategy for achieving environmental sustainability. Among these, organization-related factors include leadership commitment, an integrated approach, aligning GLSS with business strategy, a safe and healthy workplace, reward and recognition, a feedback loop, linking GLSS with integrated management systems and certifications, innovation and digitalization, and statistical thinking approach. On the other hand, employee-related factors comprise environmental training and education, workforce involvement, employee empowerment, employee environmental awareness, and teamwork. Stakeholder-related factors include government support, customer and supplier collaboration, customer and consumer awareness, recyclers and waste collectors’ involvement, collaboration with industry associations, and the role of financial institutions. Research limitations/implications The present study contributes to the existing literature through an in-depth examination of the key factors for the effective execution of the GLSS approach in a developed and developing economy and the development of a holistic model of the CSFs of a GLSS strategy for environmental sustainability linking with the ICBV. Overall, the findings can guide researchers, managers, and policymakers in understanding the CSFs of the GLSS strategy for enhancing the environmental performance of manufacturing organizations. Originality/value This study is one of the early comparative studies using the ICBV to investigate the CSFs of a GLSS strategy in the FP industry of a developed and developing economy.

Data visualization is a core part of statistical practice and is ubiquitous in many fields. Although there are numerous books on data visualization, instructors in statistics and data science may be unsure how to teach data visualization, because it is such a broad discipline. To give guidance on teaching data visualization from a statistical perspective, we make two contributions. First, we conduct a survey of data visualization courses at top colleges and universities in the United States, in order to understand the landscape of data visualization courses. We find that most courses are not taught by statistics and data science departments and do not focus on statistical topics, especially those related to inference. Instead, most courses focus on visual storytelling, aesthetic design, dashboard design, and other topics specialized for other disciplines. Second, we outline three teaching principles for incorporating statistical inference in data visualization courses, and provide several examples that demonstrate how to follow these principles. The dataset from our survey allows others to explore the diversity of data visualization courses, and our teaching principles give guidance for encouraging statistical thinking when teaching data visualization.

ABSTRACTWe report on the first year of an ongoing pilot of large‐screen interactive whiteboards (IWBs) in small group tutorial and lab classes for a major second‐year mathematical statistics subject, exploring what they may bring to our teaching of both core theoretical principles and computation/software use. In the tutorials, students worked collaboratively in small teams, hand‐writing solutions to conceptual problems, following established previous practice, but a modality well supported by IWBs. However, the labs were completely revamped to focus primarily on open‐ended team exercises, which challenged students to be creative and think laterally together, including team presentations sharing approaches. Whereas our traditional computer labs have students working individually using rows of computers on benches, the new classroom structure with IWBs enabled open‐ended, engaging and collaborative team problem‐solving supported by shared visualisation tools. As assessed by experienced teachers, student interest, engagement, conceptual understanding, presentation skills, self‐insights into their own statistical thinking and team work were all enhanced. Indeed, students entered into a genuine co‐construction of meaning through dialogue with the staff. Key lessons for statistics educators wishing to follow this path are to bring in the university IT early in the process, but without underestimating the academic staff time required to reshape approaches to realise full potential and to ensure good training and support for the tutoring staff.