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The pervasive digitization of society underscores the crucial role of data and its significant impact on decision-making across various domains. As a result, it is essential for individuals to acquire competencies in handling data. This need is particularly pertinent in K-12 education, where early engagement with data and statistics can lay a foundational understanding for future academic and professional endeavors. Additionally, K-12 education should provide students with critical skills necessary for navigating the complexities of daily life and making informed decisions in a data-rich society. This systematic review examines the state of research on statistical and data literacy in K-12 STEM (Science, Technology, Engineering, and Mathematics) education. It focuses specifically on cognitive, affective, and behavioral metrics and pedagogical approaches empirically investigated in this context. Using a rigorous selection process, we identified and synthesized 83 original empirical papers. Additionally, we invited the authors of these studies to share their perspectives on future strategies for addressing statistical and data literacy. The results indicate that the included studies primarily focus on the construct of statistical literacy, which is operationalized through a diverse array of metrics, predominantly within the context of mathematics education. We identified effective pedagogical approaches, such as authentic problem-solving and the integration of real-world data. The researchers surveyed emphasized the importance of interdisciplinary teaching, adapted curricula, and improved professional development for pre- and in-service teachers. Our findings underscore the growing relevance of this field, but suggest that integrated perspectives on statistical and data literacy within STEM subjects are limited. [ABSTRACT FROM AUTHOR]

Statistics knowledge, literacy, and skills are vital to undergraduates' academic and professional careers. Students' attitudes toward statistics play a vital role in their understanding of the importance of statistics. Therefore, it is important to examine their attitude toward statistics. This study investigated the attitude of 153 business students toward statistics and the factors that affect this attitude using the Survey Attitudes Toward Statistics (SATS‐36) scale. It also investigated the relationship between attitudinal components and students' demographic and academic characteristics. Five components underlying students' attitudes toward statistics were identified using exploratory factor analysis. Further, a background in quantitative subjects and demographic characteristics was found to affect students' attitudes toward statistics. The results suggested that statistics should be taught in a more practical way to relate it to students' lives, and teachers must adopt active learning methods in which students work with data and real‐life examples. [ABSTRACT FROM AUTHOR]

Tasks in which learners are asked to compare two data sets using box plots and decide which distribution contains more observations above a given threshold have already been investigated in research. There are indications that these tasks are solved schema-based and that different (correct and erroneous) schemas are used depending on the arrangement of the quartiles around the threshold. Erroneous schemas can cause systematic errors and are often based on typical misconceptions. For example, if learners did not complete the conceptual change and assume that in box plots - like in most other statistical representations (e.g., bar or circle diagrams) - more (box) area also represents more observations, they decide the task according to which box plot shows more box area above the threshold. However, this can lead to incorrect answers, as the box area does not represent frequency but the range of the middle half of the data (interquartile range) and thus a measure of variability. So far, these schema-based reasoning processes have mainly been investigated via differences in solution rates of congruent and incongruent items. The present study investigates whether eye-tracking data can help to better understand which information is processed in the different schemas. Our research interest is based on hypotheses specifying which box plot components are significantly involved in the different schemas. We assume that the gaze patterns of learners using different schemas differ both regarding the number and duration of fixations on the relevant box plot components (areas of interest) and in terms of the number of transitions between them. We asked N = 14 participants to solve congruent and incongruent items and simultaneously collected eye movement data. In the analysis, we first used the solution rates to assign the schemas most likely used. Subsequently, the eye-tracking data were analyzed regarding differences in line with our hypotheses. We found hypothesis-compliant effects in all schemas regarding the number of fixations and transitions, but not regarding fixation duration. These results not only validate the schemas identified in previous studies, but also indicate that the schemas differ primarily in terms of which quartile is focused. [ABSTRACT FROM AUTHOR]

Statistics modules are included in most university degrees, independent of the degree area, and this means that many students face these modules underprepared and struggle because of a lack of statistics knowledge. The Maths Support Centre (MSC) in the University College Dublin (UCD) provides support for various mathematics-related subjects, with statistics students being the second-largest cohort of visitors. The overall goal of this paper is to identify the common statistical issues students face during the transition from secondary education to tertiary education. The main data set for this study is the data from UCD students who have accessed the UCD MSC since 2015/16 for statistics support; the categorization of statistical concepts has been made with the statistics module description for each statistics subject at the Universidad Rey Juan Carlos (URJC). First, we conducted a categorization of statistical concepts taught in university (based on URJC's catergorization); after that, UCD MSC tutor comments were categorized and validated, and subsequently descriptive analyses and text mining were used on the UCD MSC comments to achieve a deeper understanding of the statistical issues. The statistical issues presented were categorized as descriptive statistics (22.8%), probability (44%), statistical inference (29.2%), and statistical software (4%). Students struggled with material that was introduced at university level rather than material seen at secondary level. Our findings on students' main statistical issues contribute to the development of a suite of evidence-based educational applications and games to support undergraduate students internationally in first- and second-year statistical modules. [ABSTRACT FROM AUTHOR]

This study argues that the works of philosopher Jürgen Habermas can provide useful directions for mathematics education research on statistical literacy. Recent studies on the critical demands posed by statistical information in media highlight the importance of the communicative component of statistical literacy, which involves students' ability to react to statistical information. By adapting Habermas' construct of communicative rationality into a framework for statistical literacy, a novel analytical tool is presented that can provide theoretical insights as well as in-depth empirical insights into students' communication about statistical information. Central to the framework are the four validity claims of comprehensibility, truth, truthfulness, and rightness which interlocutors need to address to engage in statistical communication. The empirical usefulness of the framework is shown by presenting the results of a study that examined Grade 5 students' responses to fictional arguments about the decline of Arctic sea ice. The Habermas-based framework not only reveals that complex evaluations of statistical arguments can take place even in Grade 5 but also shows that students' evaluations vary greatly. Empirical results include a content-specific differentiation of validity claims through inductively identified sub-categories as well as a description of differences in the students' uses of validity claims. [ABSTRACT FROM AUTHOR]

Abstract The pervasive digitization of society underscores the crucial role of data and its significant impact on decision-making across various domains. As a result, it is essential for individuals to acquire competencies in handling data. This need is particularly pertinent in K-12 education, where early engagement with data and statistics can lay a foundational understanding for future academic and professional endeavors. Additionally, K-12 education should provide students with critical skills necessary for navigating the complexities of daily life and making informed decisions in a data-rich society. This systematic review examines the state of research on statistical and data literacy in K-12 STEM (Science, Technology, Engineering, and Mathematics) education. It focuses specifically on cognitive, affective, and behavioral metrics and pedagogical approaches empirically investigated in this context. Using a rigorous selection process, we identified and synthesized 83 original empirical papers. Additionally, we invited the authors of these studies to share their perspectives on future strategies for addressing statistical and data literacy. The results indicate that the included studies primarily focus on the construct of statistical literacy, which is operationalized through a diverse array of metrics, predominantly within the context of mathematics education. We identified effective pedagogical approaches, such as authentic problem-solving and the integration of real-world data. The researchers surveyed emphasized the importance of interdisciplinary teaching, adapted curricula, and improved professional development for pre- and in-service teachers. Our findings underscore the growing relevance of this field, but suggest that integrated perspectives on statistical and data literacy within STEM subjects are limited.

In an increasingly data‐driven world, statistical literacy is a necessity yet statistical learning is often inhibited by statistics anxiety. Using the Auzmendi Scale to Measure Attitude toward Statistics (ASMAS), this study examines how statistics anxiety in university students is related to other dimensions of their attitudes toward statistics and how statistics anxiety and other dimensions change following introductory statistics instruction. Based on data collected from Spain, Canada, and Australia, this study finds that anxiety is negatively related to security–confidence, pleasantness, and motivation. The structure of these relationships is consistent across countries and disciplines and remains in place after statistics instruction. Further, by the end of an introductory statistics course, students report higher security–confidence and pleasantness but lower anxiety. Results thus suggest where efforts to improve students' experience with statistics might need to be directed, and the paper concludes with a discussion of the implications of these results for statistics instruction. [ABSTRACT FROM AUTHOR]

Statistical literacy is critical for all sociology students because it facilitates academic and professional success, high-paying jobs, and informed citizenship. Most students, however, lack adequate statistical literacy to engage with sociological research. Within that general deficit, there are gender, racial, and social-class differences, with students from historically marginalized groups starting and staying behind. In this conversation, we argue that to deepen statistical literacy and reduce inequalities, instructors must be willing to sacrifice breadth of content to attend to students' psychosocial needs throughout sociology curricula, especially in courses where quantitative methodology is not the core focus. We synthesize prior literature into a holistic psychosocial approach for teaching quantitative sociology content at all course levels: build interest and motivation, foster a growth mindset, develop statistical efficacy, encourage belonging, and challenge stereotypes. [ABSTRACT FROM AUTHOR]

Textbooks offer instructors an opportunity to promote data visualization and statistical literacy throughout the sociology curriculum. In this study, we examined 463 data visualizations from 27 textbooks for Introduction to Sociology, Social Problems, and intermediate elective courses to illuminate the range of data visualizations and their use of statistical data and numerical variables. We find that textbooks rely on a narrow range of data visualizations (e.g., bar/column charts and tables), statistical data (e.g., percentages), and numerical variables (e.g., age). Introduction to Sociology textbooks used more data visualizations, and Social Problems textbooks incorporated more types of statistics. In contrast, intermediate-level textbooks presented more measures of central tendency. Overall, our results suggest textbooks could better integrate a broader range of figures, statistical data, and numerical variables. We conclude with resources for integrating data visualization in instruction. [ABSTRACT FROM AUTHOR]

Statistics and probability is one branch of mathematics with numerous conceivable outcomes and problem-solving circumstances that require a basic problem-solving aptitude. The purpose of this study is to determine the need for high school mathematics teachers to teach statistics and probability, which would serve as a basis for improving the level of teaching statistics and mathematics in general. A descriptive research method was applied in this study, and a two-part survey was employed as the primary method of data collection. A total of 34 secondary school mathematics teachers from 23 private and public secondary schools in Kazakhstan acted as respondents to answer a specially designed questionnaire. The mean value, percentage of frequency, standard deviations, correlation, regression, percentage of correct answers and average percentage scores were used. Based on the responses, the respondents have a low level of knowledge regarding the content of statistics, but their students have satisfactory results in statistics and mathematics. The results of this study will give scientists an idea of how to improve the level of training of mathematics teachers. In addition, since higher educational institutions are engaged in the training of mathematics teachers, they need to think about the methodology of teaching statistics, as well as teacher training institutes, to adapt courses and seminars for teachers. [ABSTRACT FROM AUTHOR]

With increased access to biomedical and electronic health records data and the complexity of research questions, individuals in residency programmes who aim to conduct research require specialized educational programmes and biostatistics support. Biostatistics collaboration units in academic health centres often work with residents to conduct data‐intensive research. These units face numerous challenges related to providing training in statistical literacy and collaborating on resident‐led research within very restricted timelines. Since 2019, the Duke Biostatistics, Epidemiology, and Research Design (BERD) Methods Core has supported over 247 resident‐led projects by developing tools and resources to address these challenges. This manuscript presents novel processes and training materials that other institutions can use to help biostatistics collaboration units effectively support resident training programmes. We provide a framework to support the development of collaborative teams, along with specialized training materials for residents who collaborate with these teams. [ABSTRACT FROM AUTHOR]

The Social Package for the Social Sciences (SPSS) is among the most common programs used by researchers in language studies to explore, examine, and analyze quantitative data for the sake of drawing valid conclusions. The current action research aims to offer insights into the utilization of SPSS by Master’s students in their empirical studies for statistical analysis. Specifically, it attempts to depict the way SPSS is employed in academic pursuits, and discern the inherent challenges faced by students, offering insights into the obstacles that may impede their effective use of SPSS as a tool for statistical exploration and interpretation in academic research. To reach these objectives, ten Master’s dissertations written by EFL students at the Department of English at Mouloud Mammeri University in language studies were scrutinized though a qualitative content analysis. Findings indicate that the students limit the scope of their use of SPSS to quantitative variables and descriptive statistics. In addition, it has been found that the most commonly used statistical technique is ‘the mean’ or ‘the average’ that could be easily measured without any sophisticated software. Furthermore, no reference to inferential statistics in order to compare among groups or to measure the impact of a variable on another is made. The overall conclusion drawn from the analysis of the corpus is that students exhibit reluctance to the use of inferential statistics in their empirical studies and unconsciously believe that SPSS, inferential statistics, or any other sophisticated software are tightly related to natural sciences and could not be easily applied in social sciences. On the basis of the findings, a number of recommendations are suggested like organizing workshops with computer scientists. In addition, students’ self-confidence should be increased in order to change their perception of their potential and abilities to feel more at ease with statistics and technologies while doing academic research in language studies.

Statistics has played an increasingly important role in applied linguistics research. However, little research has been available that sheds light on statistical literacy among early‐career researchers of applied linguistics. The study reported in this article surveyed statistical literacy, attitudes toward statistics, and statistics self‐efficacy among Chinese applied linguistics research students and further examined the inter‐variable relationships. Based on analyses of the cross‐sectional survey of 211 master's students, we found that: (1) the students' overall statistical literacy was not satisfying; (2) although they thought positively of statistics, they were not particularly confident with statistics; (3) the three experience‐related variables—the number of statistics courses taken, quantitative research orientation, and self‐training—were important predictors of statistical literacy, attitudes toward statistics, and statistics self‐efficacy; and (4) statistics self‐efficacy seemed to mediate the relationship between attitudes toward statistics and statistical literacy. We discussed these findings in relation to improving statistics courses in applied linguistics program in China and to statistics learning and teaching. [ABSTRACT FROM AUTHOR]

Statistical literacy (hereafter SL) has been considered an important learning outcome in statistics learning in higher education, yet studies that focus on investigating the factors and conditions that influence students' SL, especially mathematics education students, are still limited. This study seeks to uncover the factors and conditions that significantly contribute to the SL of mathematics education students. This survey study involved 1,287 mathematics education students from 21 higher education institutions in Indonesia. Linear regression analysis involving four predictor variables (i.e., gender, status of higher education institution, laptop ownership, and research preference) was performed to determine the variables that contributed significantly in predicting SL achievement. The results revealed that gender, higher education institution's status, and laptop ownership contributed significantly, but research preference was not significant in predicting mathematics education students' SL. Furthermore, laptop ownership was found to have the highest contribution in predicting mathematics education students' SL. All findings and their implications are discussed. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to overview the research on concepts and teaching methods of statistical literacy and to discuss trends and issues. The concepts of statistical literacy, regarded as the ability to interpret and evaluate statistical information, have been expanded in the light of various perspectives: such as critical thinking, critical literacy, or big data trends. In addition, this paper summarized the five characteristics of teaching methods (media reports as teaching materials, instructing learners to question statistical information, discussing statistical information among students, acquiring and utilizing various forms of knowledge, and individual activities aimed at mastering how to read and interpret statistical information appropriately) with a review of previous practical research on statistical literacy. On the basis of these results, this paper concludes by proposing four future issues in the teaching methods of statistical literacy (focus on critical literacy, integration with the "enquiry contexts," consideration of the results of the research on reading strategies, promotion of collaboration among learners). [ABSTRACT FROM AUTHOR]

This article aims to evaluate how teachers, in training and in-service, define the concepts of randomness, probability, chance and variability, fundamental terms in the teaching of statistics. To this end, a printed recording protocol of natural semantic networks was applied to 16 teachers in training and 58 in-service teachers, selected through non-probabilistic sampling. The results provide evidence that the concept with the lowest conceptual density in both groups is variability. Likewise, a greater presence of similar words was observed between randomness, chance and probability, despite being different concepts. Another finding is the association of the inducing concepts to words that are used within the basic lexicon in Chilean Spanish, whose dictionary is not specialized in statistics. It is concluded about the scarce use of technical language by the participants, which would eventually affect the teaching of statistics. [ABSTRACT FROM AUTHOR]

In the 21st century, statistical thinking has become necessary for all citizens as this skill is vital for societal literacy. Students with statistical thinking will grasp and apply the problem's context to develop research and draw conclusions, as well as the coherence of the whole process from asking questions to collecting data, evaluating, and testing hypotheses. Although statistical thinking is increasingly in demand in various emerging vocations, students and teachers find statistics challenging to understand. Thus, the study examines the level of statistical thinking of the process of describing data, organizing and reducing data, representing data, and analyzing and interpreting data among high school students based on gender. The statistical thinking test modification of the framework validation test, code, and subprocess reference was used to collect research data from 35 grade 10 students. The research data were statistically analyzed using the statistical package for social sciences version 23 software through descriptive and inferential statistics, specifically an independent t-test analysis. The findings revealed that none of the students achieved an analytical level of understanding; instead, their general statistical thinking skills were at the transitional level, followed by the idiosyncratic and quantitative levels. The findings also demonstrated no statistically significant gender-related disparities in students' statistical thinking. The study proposes several recommendations, including the necessity of connecting statistical activities to the reality of students' life and area of study and emphasizing practical rather than theoretical aspects. [ABSTRACT FROM AUTHOR]

Statistical literacy is fundamental competence to think critically and to conclude information based on data. This study aims to describe students' statistical literacy according to gender, grade, and educational field. This research was a quantitative method with a cross-sectional design by explaining and analyzing the results using Rasch modeling, and students' statistical literacy was categorized and described according to statistical literacy topics, gender, student grade level, and educational field. The respondent of this study was 271 students obtained through the stratified random sampling in Yogyakarta's senior high school. The result of this study confirmed that the statistical literacy skills of students are still at a low level. The lowest aspect is the scope of conclusions, while the highest aspect is data production (medium level). There are no significant differences in statistical literacy skills based on gender and class. However, there are significant differences in statistical literacy based on field education. It indicates that policymakers or teachers should improve students' statistical literacy skills by training or applying a learning model that focuses explicitly on statistical literacy skills. [ABSTRACT FROM AUTHOR]

The Buddhist sangha played a crucial role in ancient China, exerting significant influence on its society through religious identity and cultural knowledge. However, not all members of the monastic community were literate. The Tang Dynasty introduced an examination system that assessed monks' proficiency in reciting Buddhist scriptures, determining their eligibility for ordination. Simultaneously, efforts to remove unqualified monks and nuns provided an opportunity to estimate the literacy rate within the monastic community. A statistical analysis of the literacy rate offers a novel perspective for understanding the evolution of Buddhism, the intricate relationship between religion and politics, and the role of the monastic community in local society during the Tang Dynasty. [ABSTRACT FROM AUTHOR]

The introduction of statistical concepts into school curricula in Australia and New Zealand in the early 1990s initiated an ongoing research program into the learning and teaching of statistics and probability in both countries. This paper reviews the contribution of Australian and New Zealand researchers to building statistical literacy at school, alongside international developments. From recognising how students develop understanding of specific statistical and probabilistic concepts, through teacher knowledge and beliefs for teaching statistics, to intervention studies and targeted teaching, the field of statistics education has grown and changed. Statistics and probability are now well established as part of the mathematics curriculum. The importance of linking statistical literacy and statistical understanding across the curriculum, as well as in STEM, has also begun to receive attention as other subjects have recognised the importance of data in their fields. Following a comprehensive review of the field in Australia and New Zealand, this paper then considers emerging areas of interest, such as new approaches to data visualisation, and suggests future research. [ABSTRACT FROM AUTHOR]

The teaching and learning of statistical thinking begins at a young age in Australia, with a focus on data representation and interpretation from Foundation Year (age 5), and the collection, sorting and categorising of items from the natural environment starting even earlier. The intangible concept of data, as part of statistical literacy, can be complex for children to grasp, especially when applying the notion of data to the everyday world or when data are explored in isolation to an investigation process. Authentic data modelling experiences present meaningful opportunities to apply statistical thinking although expert STEM knowledge is not always accessible to primary classroom teachers, nor is it always obvious how to implement such authentic problems within a classroom context. In this exploratory case study, we present data from a Year 4 classroom (age 9) statistical investigation addressing, 'How big is a leaf?' linking data to the real-life STEM context they represented. The authors were interested in how the teacher's communication processes supported her students' emerging understandings about data. Wit's (2018) cognitive tuning framework offered a way to capture how the communication processes in a group build to a commonly shared frame of reference. Findings revealed a pattern of communication between the teacher and students, supporting students' changing conceptions of data and related statistical thinking processes, throughout the investigation. [ABSTRACT FROM AUTHOR]

The capacity to interrogate data with critical thinking is a strong predictor of statistical literacy (SL). This data interrogation, from the data consumers' perspective, incorporates four complex response skills: interpreting, communicating, evaluating, and decision-making, and those skills are strongly supported by students' appreciation of three interrelated knowledge components (text and context, representation, and statistical-mathematical knowledge). Due to the need to be critical data-information readers, students' SL should develop during their formal schooling. The aim of this paper was to investigate differences in SL between Indonesian year 9 and year 12 students and between female and male students. The same test was administered to 48 year 9 students (50% females) and 48 year 12 students (50% females) from 16 different schools in Indonesia. Findings revealed that the highest percentage of year 9 and 12 students demonstrated evidence of consistent but non-critical thinking (level 4), suggesting that they exhibited their statistical knowledge but not in critical ways. There were 42% of year 9 students showing limited statistical thinking (levels 1 to 3) compared to 17% of year 12 students. Furthermore, while there were no significant gender differences in students' SL and its all skills, the study shows significant grade level differences in overall SL as well as in its skills except interpreting. Implications of this study include the development of a framework that provides a coherent assessment of students' SL from a data consumers' perspective, along with suggestions for classroom teaching. [ABSTRACT FROM AUTHOR]

The Social Package for the Social Sciences (SPSS) is among the most common programs used by researchers in language studies to explore, examine, and analyze quantitative data for the sake of drawing valid conclusions. The current action research aims to offer insights into the utilization of SPSS by Master's students in their empirical studies for statistical analysis. Specifically, it attempts to depict the way SPSS is employed in academic pursuits, and discern the inherent challenges faced by students, offering insights into the obstacles that may impede their effective use of SPSS as a tool for statistical exploration and interpretation in academic research. To reach these objectives, ten Master’s dissertations written by EFL students at the Department of English at Mouloud Mammeri University in language studies were scrutinized though a qualitative content analysis. Findings indicate that the students limit the scope of their use of SPSS to quantitative variables and descriptive statistics. In addition, it has been found that the most commonly used statistical technique is ‘the mean’ or ‘the average’ that could be easily measured without any sophisticated software. Furthermore, no reference to inferential statistics in order to compare among groups or to measure the impact of a variable on another is made. The overall conclusion drawn from the analysis of the corpus is that students exhibit reluctance to the use of inferential statistics in their empirical studies and unconsciously believe that SPSS, inferential statistics, or any other sophisticated software are tightly related to natural sciences and could not be easily applied in social sciences. On the basis of the findings, a number of recommendations are suggested like organizing workshops with computer scientists. In addition, students’ self-confidence should be increased in order to change their perception of their potential and abilities to feel more at ease with statistics and technologies while doing academic research in language studies. [ABSTRACT FROM AUTHOR]

Tasks in which learners are asked to compare two data sets using box plots and decide which distribution contains more observations above a given threshold have already been investigated in research. There are indications that these tasks are solved schema-based and that different (correct and erroneous) schemas are used depending on the arrangement of the quartiles around the threshold. Erroneous schemas can cause systematic errors and are often based on typical misconceptions. For example, if learners did not complete the conceptual change and assume that in box plots – like in most other statistical representations (e.g., bar or circle diagrams) - more (box) area also represents more observations, they decide the task according to which box plot shows more box area above the threshold. However, this can lead to incorrect answers, as the box area does not represent frequency but the range of the middle half of the data (interquartile range) and thus a measure of variability. So far, these schema-based reasoning processes have mainly been investigated via differences in solution rates of congruent and incongruent items. The present study investigates whether eye-tracking data can help to better understand which information is processed in the different schemas. Our research interest is based on hypotheses specifying which box plot components are significantly involved in the different schemas. We assume that the gaze patterns of learners using different schemas differ both regarding the number and duration of fixations on the relevant box plot components (areas of interest) and in terms of the number of transitions between them. We asked N = 14 participants to solve congruent and incongruent items and simultaneously collected eye movement data. In the analysis, we first used the solution rates to assign the schemas most likely used. Subsequently, the eye-tracking data were analyzed regarding differences in line with our hypotheses. We found hypothesis-compliant effects in all schemas regarding the number of fixations and transitions, but not regarding fixation duration. These results not only validate the schemas identified in previous studies, but also indicate that the schemas differ primarily in terms of which quartile is focused.

Understanding the margin of error (MoE) as a part of statistical literacy which is useful for the public to select credible information from various surveys and polls. The study aims to reveal the levels of statistical literacy of mathematics education students, especially in understanding MoE, and compare them based on four variables: gender, enrollment in a statistics course, year in the program, and type of university. The online survey research involved undergraduate students of the mathematics education study program from 21 universities in Indonesia’s western, central, and eastern regions as the sample (n = 970). Descriptive statistics was used to describe the literacy levels and inferential statistics (t-test and F-test) to compare them based on the four variables. The results of the study reveal that: (1) student literacy in understanding the MoE concept is dominant at the non-literate level; and (2) there are significant differences in students’ literacy levels in terms of gender, enrollment in a statistics course, year in the program, and type of university. The study indicates that the literacy of mathematics education students is still low, so the statistics course is expected to focus more on developing statistical literacy. [ABSTRACT FROM AUTHOR]

Statistical and data literacy have emerged as key competencies in the 21st-century. Science, technology, engineering, and mathematics (STEM) curricula have been adapted in various countries to place increased emphasis on proficient data use and statistical comprehension. Consequently, there is a need for an adequate education and professional training of STEM teachers. This systematic review analyzes and synthesizes research on teachers' statistical and data literacy in K-12 STEM education. A total of 42 articles were analyzed, with a specific focus on teacher variables, teacher education, and their impact on classroom practice. The findings emphasize the recent emergence of research in this domain, particularly focusing on statistical literacy, with mathematics being the dominant STEM domain explored. Most studies examined cognitive variables of both pre-service and in-service teachers, revealing relevant gaps in knowledge and skills related to statistical and data literacy. Limited attention was given to teachers' affective variables. Research on the complex relationships between teacher variables, their teaching practices, and their students' learning is scarce, necessitating further investigation. Several pedagogical approaches to prepare STEM teachers for promoting statistical and data literacy in their classrooms were identified. Positive impacts on teachers' cognitive and affective variables were observed, but longitudinal effects and transfer to classroom practice require further inquiry. As curricula worldwide increasingly emphasize statistical and data literacy, this review underscores the necessity of enhancing teachers' competencies, advocates for tailored pedagogical approaches, and emphasizes the need for further research to shape effective instruction in K-12 STEM education. [ABSTRACT FROM AUTHOR]

We explore the effect of the design of intentional teaching sequences from the concrete to the abstract in order to promote the teaching of statistics in connection with sustainability. To this end, the pre-validated questionnaires "MTSK-Stochastic Statistics" and "ESD in Statistics Activities" are administered to in-service primary education teachers. Both tools contain the same statistics teaching itinerary, based on the mathematics teaching itineraries approach, that includes five tasks: a real situation, a manipulative, a game, a technological resource, and a graphic resource. Each task includes an overview, objectives, content, required materials, development, and development questions; in addition, the second questionnaire includes connections to sustainability. The results show that tasks with real contexts, manipulative resources, and technological resources are the ones with the lowest scores. These initial findings lead us to conclude that teachers mobilize their knowledge to teach statistics as it relates to sustainability in different ways, depending on the type of task. [ABSTRACT FROM AUTHOR]

An anonymous survey was given to n = 73 students in an asynchronous online statistical literacy course at a mid-sized Hispanic Serving Institution. Informed by teaching experience, literature on lexical ambiguity, and everyday usage of statistics words and phrases, the first author designed the survey to yield insight into how students view phrases such as "average person," "random person," and "outlier person," and to explore possible connections or patterns with such phrases. Findings suggest that students view phrases such as "random person" in a way much further from standard usage than they do phrases such as "anonymous person." Considerations such as diversity, agency, and variable identification are identified as possibly affecting usage and meriting further investigation. Recommendations for teaching and future research are provided. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

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The development of statistical literacy has become a key outcome of mathematics curricula in various countries. The purpose of the study was to explore primary pre-service teachers' (PSTs') knowledge of aspects of measures of central tendency, which is a central concept in statistical literacy. The 183 PSTs' written responses to four tasks were analysed in terms of demands elucidated by the Mathematical Knowledge for Teaching (MKT) framework. The results showed that almost all the participants were able to carry out simple computations of the mean and median and were generally able to identify learner errors. However, they found it much harder to solve problems that required unpacking of the procedures and comparison of different measures of central tendency. They also struggled with providing suitable feedback based on learner errors. This study shows that primary school PSTs need more support in developing these MKT skills with respect to the concepts of the mean and median. [ABSTRACT FROM AUTHOR]

The study reported on in the article examines the patterns and trends of statistical analysis in translation and interpreting (T&I) research, based on a longitudinal quantitative analysis of more than 3300 research articles sampled from eleven leading T&I journals (2000–2020). This evidence-based review is the first study to provide a systematic mapping of statistical methods used by T&I researchers. Our analyses suggest that (a) about 40% of the articles use statistics, and the use of statistics has been increasing over time; (b) the most frequently used inferential statistical techniques are the t-test, Pearson's correlation, and chi-squared test; and (c) although the use of statistical methods has become increasingly diversified, about 90% of the methods used are basic-level statistics. We discuss these findings in relation to statistical teaching and learning for relevant stakeholders, especially T&I researchers. [ABSTRACT FROM AUTHOR]

Statistical literacy (hereafter SL) has been considered an important learning outcome in statistics learning in higher education, yet studies that focus on investigating the factors and conditions that influence students’ SL, especially mathematics education students, are still limited. This study seeks to uncover the factors and conditions that significantly contribute to the SL of mathematics education students. This survey study involved 1,287 mathematics education students from 21 higher education institutions in Indonesia. Linear regression analysis involving four predictor variables (i.e., gender, status of higher education institution, laptop ownership, and research preference) was performed to determine the variables that contributed significantly in predicting SL achievement. The results revealed that gender, higher education institution’s status, and laptop ownership contributed significantly, but research preference was not significant in predicting mathematics education students’ SL. Furthermore, laptop ownership was found to have the highest contribution in predicting mathematics education students’ SL. All findings and their implications are discussed.

All statistical methods involve assumptions about the data and the output of the methods can be biased when the assumptions are not supported by the data. One of the common assumptions is equal variance across the conditions. Another common assumption is that variables are independently sampled from identically distributed populations (i.i.d.). The present study describes an example of such a violation of these assumptions and its effect on the results of Bayesian statistical analyses. Yu et al. (2021) developed a Bayesian statistical model that can analyze the same type of data as the one-way repeated-measure ANOVA. Their model assumed equal variance and i.i.d. Unfortunately, these assumptions were not satisfied by their data. In the present study, their model was revised to allow variance to vary with the conditions, and their data was reanalyzed. The results of the analyses using these models were compared with the psychophysical results of Yu et al. (2021). This comparison showed that the violated assumptions biased the results of the analysis. This bias made the results of the analysis appear more supportive of Yu et al.’s (2021) conclusion, but the validity of the analysis’s results needs to be re-considered. Note that it is important that one carefully scrutinizes the data and understands the statistical method used to discuss the results of the analysis.

AbstractWe present a unique and innovative course, Biostatistical Literacy, developed at the University of Minnesota. The course is aimed at public health graduate students and health sciences professionals. Its goal is to develop students’ ability to read and interpret statistical results in the medical and public health literature. The content spans the typical first-semester introductory material, including data summaries, hypothesis tests and interval estimation, and simple linear regression, as well as material typically presented in a second introductory course, including multiple linear regression, logistic regression, and time-to-event methods. The focus is on when to use a method and how to interpret the results; no statistical software computing is taught. A flipped classroom approach is used, where students are first exposed to the material outside of class, and class time is devoted to actively exploring and applying the concepts in greater depth. The course structure, the class activities, and feedback from students will be shared. Supplementary materials for this article are available online.