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Statistical literacy, encompassing the interpretation and evaluation of statistical reports, is a skill developed in schools. However, the pedagogical approaches to statistical literacy skills have not been sufficiently investigated. This study investigated whether statistical literacy skills could be demonstrated through high school lessons. These lessons include a statistical literacy process, grounded in critical thinking principles, to facilitate students' interpretation and evaluation of statistical reports. Additionally, a corresponding worksheet, aligning with this process, is included in the lessons to help students comprehend statistical reports. Seven 50-min lessons were conducted with 34 high school students, aged between 17 and 18 years, using this worksheet. Additionally, a statistical literacy skills test, designed to assess various statistical literacy skills within a short timeframe, was developed and administered at the beginning of the first lesson and at the end of the last lesson. The outcomes revealed that students were able to demonstrate some skills and effectively evaluate statistical reports. This study makes notable contributions by introducing a structured statistical literacy process and an associated skills assessment test. These tools can serve as valuable resources for students seeking to critically evaluate statistical reports and enable educators to gauge the extent of students' acquisition. for this article are available online. [ABSTRACT FROM AUTHOR]

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]

Students can learn to think statistically by being actively engaged in investigations that use real data to answer a question. Projects that explore familiar questions that students intuitively understand can spark their interest and are ideal vehicles to teach data literacy. This article shares a project that was used in an introductory statistics course to investigate if young people were sleep-deprived or had altered their sleep schedules during the COVID-19 pandemic. Students’ interest was piqued and they were highly motivated to learn how the pandemic had impacted their peers. Working with an authentic, relatable context, helped students make conjectures. I describe how students used descriptive and inferential statistics to understand whether their peers’ sleep schedules had changed during the pandemic. Pedagogical practices that are shared helped build students’ confidence and decrease the sense of overwhelm that can accompany comprehensive investigative projects. In addition to the significant gains students made, modeling how statistics research is done outside the classroom, investigative projects such as this can provide teachers with valuable insights and an alternative way to assess students’ ability to think like empirical researchers.

Statistical literacy is an essential competency in today’s data-driven society. However, research in this field often overlooks primary-level students, focusing predominantly on those in higher education. To address this gap, the present study evaluated the statistical literacy of 50 fifth-grade students in Indonesia, with a focus on their abilities in data analysis and interpretation. Utilizing a descriptive cross-sectional design, the study employed a written test grounded in real-world data scenarios, adapted from established theoretical frameworks. The assessment included various data representations—pictograms, bar charts, tables, and line charts—designed to evaluate students’ skills in comprehension, comparison, and interpretation. The results indicate that the majority of students operated at an Idiosyncratic level, demonstrating limited capacity for data interpretation, while a smaller proportion reached the Transitional level; notably, no students attained Quantitative or Analytical proficiency. These findings underscore the urgent need for curriculum reforms to enhance both computational and analytical skills at an early stage, thereby equipping students with the foundational competencies required for advanced data reasoning. Future research should investigate pedagogical strategies that can bridge early-stage statistical literacy with the demands of higher-level statistical reasoning, ensuring students are better prepared for a data-intensive world.

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.

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]

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.

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.

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]

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 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]

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.

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.

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]

The Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report advocates for use of real data with context and purpose. This work contributes to the growing literature on assessing statistical literacy by investigating the influence of context as it relates to assessment performance among post-secondary introductory statistics students. We discuss the development of an isomorphic form of an existing assessment instrument, and report results which concluded that test takers demonstrated lower statistical literacy scores when assessment tasks incorporated real data from published studies as context when compared with functionally similar tasks such as those with a contrived data set and a realistic context. [ABSTRACT FROM AUTHOR]

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Abstrak Literasi statistik merupakan kemampuan menggunakan statistika dalam kehidupan sehari-hari dan merupakan kemampuan penting untuk menghadapi revolusi industri 4.0. Penelitian ini bertujuan untuk mengemukakan kondisi, permasalahan literasi statistik siswa dan bagaimana permasalahan belajar statistika yang dialami siswa di sekolah serta sejauh mana pengetahuan dan kemampuan siswa terkait literasi statistik. Jenis penelitian yang digunakan yaitu penelitian kualitatif dengan pendekatan deskriptif dan desain penelitian fenomenologi. Partisipan dalam penelitian ini 11 siswa SMA di kota Lhokseumawe, Aceh. Pengumpulan data dilakukan melalui wawancara dan satu set soal tes kemampuan literasi statistik. Analisis data hasil wawancara dilakukan melalui analisis tematik dan analisis data untuk soal tes kemampuan literasi statistik dilakukan melalui teknik interaktif. Hasil wawancara pada penelitian ini diperoleh 4 temuan utama tentang kondisi literasi statistik siswa yaitu persepsi siswa mengenai materi yang termasuk ke dalam stastistika, implementasi literasi statistik dalam kehidupan sehari-hari, pelaksanaan pembelajaran statistika di sekolah dan hambatan siswa mempelajari statistika. Sedangkan berdasarkan hasil tes diperoleh tingkat kemampuan literasi statistik siswa masih tergolong rendah. Fenomena yang diperoleh bahwa kondisi literasi statistik siswa belum baik dan tingkat kemampuan literasi statistik siswa masih tergolong rendah karena terdapat permasalahan siswa dalam mempelajari statistika. Kata kunci: fenomena; literasi statistik; pembelajaran matematika  Abstract Statistical literacy is the ability to use statistics in everyday life and is an important ability to face the industrial revolution 4.0. This study aims to describe the conditions, problems of students' statistical literacy and how problems learning statistics are experienced by students at school as well as the extent to which students' knowledge and abilities are related to statistical literacy. The type of research used is qualitative research with a descriptive approach and a phenomenological research design. The participants in this study were 11 high school students in the city of Lhokseumawe, Aceh. Data collection was carried out through interviews and a set of statistical literacy ability test questions. Data analysis from interviews was carried out through thematic analysis and data analysis for statistical literacy ability test questions was carried out through interactive techniques. The results of interviews in this study obtained 4 main findings regarding the condition of students' statistical literacy, namely students' perceptions of material included in statistics, implementation of statistical literacy in everyday life, implementation of learning statistics in schools and students' barriers to learning statistics. Meanwhile, based on the test results, it was found that the statistical literacy ability of students was still relatively low. The phenomena obtained is that the condition of students' statistical literacy is not good and the level of students' statistical literacy ability is still relatively low because there are students' problems in studying statistics.  Keywords: phenomena; statistical literacy; mathematics learning

In order to make an informed, evidence-based decision, it is vital to recognize numbers, statistics and concepts that are apparently transparent but are not always adequately accounted for. Health professionals, patients, and others frequently misinterpret numbers, statistics and concepts in healthcare. Among the many repercussions of health literacy, appropriate decision-making and the reduction in the number of interventions and treatments stand out, resulting in an improvement in people's health and a decrease in overtreatment and health expenses. This study intends to evaluate how properly health professionals and the public, in general, comprehend and interpret some health-related numbers. To accomplish this goal, the researchers shared a questionnaire made available online in Portugal from January 2, 2019, until April 12, 2019. The final sample comprised 485 respondents; 154 physicians, 142 nurses, and 189 people from other professions. The findings suggest that there is a problem with widespread numerical illiteracy, which should not be the case, highlighting the need to improve the numerical and statistical health literacy of both health professionals and the general population. So, medical professionals and patients must thus comprehend the statistics and health-related concepts to obtain the proper consent.

This research aims to identify students' statistical literacy abilities, and this type of research is descriptive qualitative. The subjects in this research consisted of 24 Sociology Education students from Muhammadiyah University of Kupang taking statistics courses in the 2023/2024 academic year. The instrument used is an assignment consisting of 2 questions. Data analysis was done by analyzing students' answer documents to complete the assignments given. Students' statistical literacy abilities are identified using five statistical literacy competencies: understanding statistical concepts, insight into the application of statistical concepts, calculation and graphing skills, interpretation abilities, and visualization and communication skills. The research results show that more than 50% of students can fulfill statistical literacy competencies such as understanding statistical concepts, numeracy, interpretation, and communication skills. However, less than 50% of students do not meet competency in insight into the application of statistical concepts and visualization skills. Students' abilities in essential statistical literacy competencies still need to be improved because most students still have limitations in these competencies.

We 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. for this article are available online. [ABSTRACT FROM AUTHOR]

Statistical literacy is an essential competence that people must face in the era of big data and Society 5.0. In Indonesia and several countries, statistics is taught as a list of competencies in mathematics subject in primary and secondary schools. This study aimed to identify whether statistical tasks in higher secondary school mathematics textbooks support statistical literacy. The qualitative data were collected via deductive content analysis, with a specific framework, toward five Compulsory Mathematics textbooks used in Indonesia. We found that statistical exercises and problems in these textbooks were dominated by data analysis type, emphasizing calculating statistics from raw data and its modification. Regarding data visualization, almost all textbooks introduced the histogram and ogive, while some also introduced the boxplot, stem-and-leaf plot, and dot plot. Improvement could be made by adding more exercises and problems related to the interpretation of statistics, evaluation of statistical results, and comparison of statistics from several data groups.

As the key to STEM education, teachers' level of STEM literacy plays a crucial role in teaching and learning. The purpose of this study is to assess the current STEM literacy of science teachers in China. This is a survey study using a questionnaire instrument to gain both quantitative and qualitative data. The quantitative data were analyzed by Statistical Package for the Social Science (SPSS) to evaluate the status of STEM literacy among China's school science teachers. Interpretive methods were applied for qualitative data analysis to support the quantitative data. The researcher found that current science teachers generally have a poor understanding of STEM education, lack STEM knowledge and teaching skills, but have the awareness to improve their own literacy. It is hoped that by investigating the STEM literacy of science teachers, the research will have a positive impact on improving science education and is expected to contribute to the development of science education in China. [ABSTRACT FROM AUTHOR]

Copyright of Avances de Investigación en Educación Matemática is the property of Sociedad Espanola de Investigacion en Educacion Matematica 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.)

IntroductionVaccination is an important measure used to control the spread of COVID19. The estimation of risk versus benefit of vaccination is based on the understanding of information about the vaccine. Statistics are frequently part of communications about COVID19. Individuals that do not have an adequate foundation of statistical knowledge may not be able to properly assess associated risks and benefits. This study aims to assess the association between statistical literacy and hesitation to receive the COVID19 vaccine.MethodsA nationally representative sample of 2,138 adults, recruited through CINT United States, Inc., (Lawrenceville, NJ; http://www.cint.com), completed an internet survey in the summer of 2021. This survey collected demographic measures and information about COVID19 vaccination status. The competency of respondents on various basic statistical concepts was assessed along with the corresponding confidence of respondents in their answers. A multivariable logistic regression model was constructed to assess the relationship between vaccine hesitancy and statistical literacy while controlling for covariates of interest.ResultsStatistical literacy was found to have a negligible association with COVID19 vaccine hesitancy (OR 1.01; 95% CI 1.00–1.02). In addition, differences in the proportion receiving the COVID19 vaccine between political affiliations, income levels, race groups, and ethnicities were observed.DiscussionThe statistical knowledge of the general American public is not commensurate with the need to be literate in basic statistical concepts in the data-driven world in which we live. An effective way to stem vaccine hesitancy may rely on increased statistical knowledge to not be biased by preconceived beliefs shaped by misinformation.

The importance of students' mastery of statistics is supported by the inclusion of this material in the mathematics curriculum since primary and secondary education. The role of statistics itself has penetrated into various professions and fields of science. Regarding statistical mastery students need to have statistical literacy skills in the form of basic skills in reading graphs, graph titles or themes, giving meaning to units of graphs, finding certain values or units, determining maximum and minimum values or the difference between the two. This study aims to describe students' errors in solving statistical literacy test similar to minimum competency assessment or asesmen kompetensi minimum (in Indonesian) based on the Kastolan stages. The type of this study is qualitative research with a case study design. The subjects of this research were 5 students of grade 11, a senior high school in Banjarbaru, South Borneo, selected using purposive sampling technique. Data were collected using tests and interviews. During the study, it was found that errors made by students include conceptual errors, procedural errors, and technical errors. Students' errors in solving statistical literacy test can have an impact on their daily life when they are dealing with data. Moreover, errors made by students when solving problems can be an early sign that there are learning obstacles experienced by students.

AbstractWe advocate for greater emphasis in training students about data management, within the context of supporting experience in reproducible workflows. We introduce the Lancaster University STatistics REsources (LUSTRE) package, used to manage student research project data in psychology and build capacity with respect to data acumen. LUSTRE provides a safe space to engage students with open research practices—by making tangible different phases of the reproducible research pipeline, while emphasizing its value as a transferable skill. It is an open-source online data catalogue that captures key data management information about a student research project of potential relevance to data scientists. Embedded within a taught programme, it also highlights concepts and examples of data management processes. We document a portfolio of open teaching resources for LUSTRE, and consider how others can implement or adapt them to facilitate data management and open research. We discuss the role of LUSTRE as a; (a) resource and set of activities for promoting good data management practices; (b) framework to enable the delivery of key concepts in open research; (c) an online system to organize and showcase project work.

Teachers’ planning as a research area still receives little attention. This study focuses on developing a research-informed framework to assess teachers’ statistics lesson plans. Using a design-based research approach, we developed a framework that includes (a) two criteria related to learning intentions (clear and comprehensive), (b) four criteria related to task features (statistical literacy, statistical investigation, real data, multiple representations), and (c) one related to constructivist lessons. Then, the team provided professional development on statistical literacy and ways to elicit and develop it in the classroom. After that, the teachers discussed the framework and used it to revise their lesson plans. Also, teachers provided feedback to revise the framework. We discuss how the framework can offer a tool for researchers to examine teacher planning competence and teachers to reflect on their practice.

In this new era drenched with data, statistical literacy becomes more essential for individuals to be able to read, communicate, and make informed decisions. Moreover, statistical literacy is highly essential for education policy makers who are highly accountable for all policy outcomes including school improvement, resource allocation, curriculum planning and intervention. Hence, there is a need to understand their perceptions and beliefs. The aim of this study is to explore whether attitude towards statistics and statistical anxiety are related to the education policy makers' statistical literacy. Considering that statistics coursework is the basis and major contributor to a statistically literate society, real problems with statistics are likely due to non-cognitive factors, which include attitudes or beliefs towards statistics. There is a global increase in literature exploring beliefs and attitudes of teachers towards statistics, indicating that studies on attitudes towards statistics do not stop at the students' level but should also be extended to education personnel who uses statistics in their workplace. While pre-service teachers in college claimed that statistics anxiety is the main obstacle to get their teaching degree. This is alarming as they are the future teachers and education policy makers with anxiety may develop avoidance to read educational diagnoses containing statistical information. Participants self-reported their statistical literacy with 20 multiple choice items tailor made to the work of education policy makers. Data were drawn from a survey elicited using a cross-sectional method on 328 education personnel working at different levels in Ministry of Education. The findings show that attitude towards statistics is not significantly related to statistical literacy while statistics anxiety has a significant negative relationship with statistical literacy. Statistical anxiety also has a negative significant relationship with attitudes towards statistics. These findings help strengthen Model of Statistical Literacy, where dispositional element including beliefs and attitude was addressed while confirming Anxiety Expectation Model. Future studies to explore other potential predictors of statistical literacy and suggested to investigate possible difference in attitude towards statistics between adult workers and students. [ABSTRACT FROM AUTHOR]

Objective: This research aimed to investigate the data literacy level of postgraduate students of the University of Tabriz and the factors affecting it. Methods: According to the purpose of the research, it is applied and in terms of the type, the method of collecting and analyzing the data is a descriptive survey. The study population of this research was post-graduate students of the University of Tabriz which were 6702 students, and according to the table of Krejci and Morgan, the number of 363 students was estimated as the sample size. The sampling method was the stratified random sampling. The research tool was a questionnaire developed by Ganbari (2019), which was designed based on the dimensions of data understanding, collection and organization, management, evaluation, and use of data. Kruskal-Wallis and Mann-Whitney statistical tests and Spearman's correlation coefficient were also used to analyze the data. Results: The results of the research showed that the level of data literacy of the students participating in the research for the questions related to data literacy was 4.0967 and it is at a good level. In addition, it was found that there is no significant difference in the level of data literacy of students in terms of variables such as gender, scientific field, and field of study. However, the results of this research showed that there is a significant difference in the level of data literacy of the given students in terms of variables such as different age ranges, master's degree, and doctoral levels, as well as between the selection or non-selection of the Proposal Unit and the different stages of thesis compilation and writing. Also, in addition to the background factors affecting the level of data literacy, a significant relationship was observed between the holding of university classes, educational workshops, and informal classes and the level of data literacy of students. Conclusion: Based on the results of this research, it can be concluded that due to the significant difference in data literacy among students of different educational levels, and students who have entered the thesis/dissertation stage and the existence of a significant relationship between the educational courses completed by the students and their level of data literacy, there should be the necessary effort to strengthen the courses that cause practical involvement of students in the thesis/dissertation to increase their level of data literacy in different universities of Iran. [ABSTRACT FROM AUTHOR]

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Statistical literacy is key in this heavily polarized information age for an informed and critical citizenry to make sense of arguments in the media and society. The responsibility of developing statistical literacy is often left to the K-12 mathematics curriculum. In this article, we discuss our investigation of K-8 students’ current opportunities to learn statistics created by state mathematics standards. We analyze the standards for alignment to the Guidelines for the Assessment and Instruction in Statistics Education (GAISE II) PreK-12 report and summarize the conceptual themes that emerged. We found that while states provide K-8 students opportunities to analyze and interpret data, they do not offer many opportunities for students to engage in formulating questions and collecting/considering data. We discuss the implications of the findings for policy makers and researchers and provide recommendations for policy makers and standards writers.

Statistics is a transversal matter in a wide range of knowledge areas. Although statistics is critically important for college students, most of them struggle when studying. Negative attitudes towards statistics can explain the weak academic performance that has been historically observed in college students. A non-probabilistic sample of 384 undergraduate students from a Spanish private university (Región de Murcia) recruited by a convenience sampling answered the Attitudes towards Statistics (Auzmendi, 1992) scale twice (at the beginning and at the end of the course). Two models were analyzed by using parallel analysis and confirmatory factorial analyses, the original version and a short version including only 15 items. The short version scores correlated positively and significantly with students grades in the mid-term and at the end of the course on data analysis. The short version showed acceptable reliability (in terms of internal consistency and temporal stability) and factorial structure. As a result, the short version is considered more parsimonious than the original version and optimal to use in educative context and for research purposes. [ABSTRACT FROM AUTHOR]

In different scientific areas, empirical studies are typically carried out by statistical null hypothesis tests. Despite the long tradition of applications, misinterpretations and misuses of the concept have led to a substantial confidence crisis in its inferential quality. One of the discussed issues is the significance-relevance discrepancy of the results of standardly applied zero-effect null hypothesis tests. This means that statistically significant test results do not automatically also have to be of scientific relevance in the specific research context. Therefore, this article is aimed at practitioners of empirical research who might want to include the aspect of practical relevance in their statistical conclusions. Different approaches to include this aspect in the inferential process are discussed with an example from the field of educational research. [ABSTRACT FROM AUTHOR]

In order to make an informed, evidence-based decision, it is vital to recognize numbers, statistics and concepts that are apparently transparent but are not always adequately accounted for. Health professionals, patients, and others frequently misinterpret numbers, statistics and concepts in healthcare. Among the many repercussions of health literacy, appropriate decision-making and the reduction in the number of interventions and treatments stand out, resulting in an improvement in people's health and a decrease in overtreatment and health expenses. This study intends to evaluate how properly health professionals and the public, in general, comprehend and interpret some health-related numbers. To accomplish this goal, the researchers shared a questionnaire made available online in Portugal from January 2, 2019, until April 12, 2019. The final sample comprised 485 respondents; 154 physicians, 142 nurses, and 189 people from other professions. The findings suggest that there is a problem with widespread numerical illiteracy, which should not be the case, highlighting the need to improve the numerical and statistical health literacy of both health professionals and the general population. So, medical professionals and patients must thus comprehend the statistics and health-related concepts to obtain the proper consent. [ABSTRACT FROM AUTHOR]

In this article, we examine the conceptual knowledge of 12th-grade students in the field of descriptive statistics (hereafter statistics), how their knowledge is aligned with the output requirements, and how they can apply their conceptual knowledge in terms of means, graphs, and dispersion indicators. What is the proportion and the result of their answers to (semi-)open questions for which they have the necessary conceptual knowledge, but which they encounter less frequently (or not at all) in the classroom and during questioning? In spring 2020, before the outbreak of the pandemic in Hungary, a traditional-classroom, "paper-based" survey was conducted with 159 graduating students and their teachers from 3 secondary schools. According to the results of the survey, the majority of students have no difficulties in solving the type of tasks included in the final exam. Solving more complex, open-ended tasks with longer texts is more challenging, despite having all the tools to solve them, based on their conceptual knowledge and comprehension skills. A valuable supplement to the analysis and interpretation of the results is the student attitudes test, also included in the questionnaire. [ABSTRACT FROM AUTHOR]

Copyright of Revista Complutense de Educación is the property of Universidad Complutense de Madrid 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.)

Textbooks are considered essential, providing a hierarchical organisation of knowledge, forging the intellectual scaffolding of students and teachers alike, and playing a crucial role in compulsory education. In this paper we discuss, by means of a content analysis, the systematic errors detected in the presentation of questions related to statistics and probability in Spanish secondary school textbooks on mathematics. We found some errors appear systematically in the texts, and the most common are: faulty differentiation between quantitative and qualitative variables, between discrete and continuous variables and between randomness and determinism, confused examples for the bar charts, uncritical choice for graphic representations, inaccuracies in specific vocabulary, and ignoring prior probabilities and a poor consideration about representativeness. We classify the observed errors considering that some of these errors arise from the inherent difficulty of the content and others arise from differences between mathematical and statistical thinking as well as from judgments based on heuristic rules. Knowing the existence of these errors and the reasons why they occur are key points to make them disappear from statistical lessons and to help citizens achieving true statistical literacy. [ABSTRACT FROM AUTHOR]

The purpose of this study was to obtain an overview of the student's Statistical Literacy model in Statistics learning in Higher Education. Researchers conducted an in-depth study of student statistical literacy, how they understand and apply statistics, how statistics are used as a tool for reliable data that can be trusted as scientific works. The research method uses qualitative research methods with a Grounded Theory approach. Participants involved in this study were 114 participants from several universities in West Java, Indonesia. The results of this study found a student statistical literacy model consisting of 2 dimensions and 5 elements. Dimensions of Statistical Knowledge: Descriptive Statistics, Inference Statistics, Statistical Communication and Statistical Reasoning. Attitude Dimensions: Confidence and Critical Attitude.

Problem and goal. Building statistical, mathematical, computational and research literacies in teaching school subjects is discussed in the article. The purpose is to develop a model for generating data for research experiments by students. Methodology. The Netlogo data generation and consecutive statistical data procession in CODAP and R programming language were used. Results. The generative approach helps students to work with data collected by agents, programmed by students themselves. In doing so, the student assumes the position of a researcher, who plans an experiment and analyses its results. Conclusion. The proposed approach of data generation and analysis allows to introduce the student to the contemporary culture of generating and sharing data.