{"title":"Visualizing a bivariate discrete distribution and other distributions derived from it","authors":"Jyotirmoy Sarkar, Mamunur Rashid","doi":"10.1111/test.12370","DOIUrl":"https://doi.org/10.1111/test.12370","url":null,"abstract":"A single discrete random variable is depicted by a stick diagram, a 2D picture. Naturally, to visualize a bivariate discrete distribution, one can use a bivariate stick diagram, a 3D picture. Unfortunately, many students have difficulty understanding and processing 3D pictures. Therefore, we construct an alternative 2D disc plot to depict the bivariate distribution of (, ), from which we obtain graphically the conditional distributions of given , and given ; the marginal distributions of , , , . Furthermore, we depict the mean and the standard deviation of each distribution using a single‐headed arrow. We hope these visualizations will help students better comprehend these concepts and avoid some misconceptions.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"16 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using practical programming tasks to enhance combinatorial understanding","authors":"Sigal Levy, Yelena Stukalin, Nili Guttmann-Beck","doi":"10.1111/test.12369","DOIUrl":"https://doi.org/10.1111/test.12369","url":null,"abstract":"Probability theory has extensive applications across various domains, such as statistics, computer science, and finance. In probability education, students are introduced to fundamental principles which may include mathematical topics such as combinatorics and symmetric sample spaces. Students pursuing degrees in computer science possess a robust foundation in programming, software engineering, and algorithmic thinking. Despite entering probability courses with a unique perspective and learning potential, these students encounter challenges in grasping combinatorial concepts. In this experiment, we challenged first-year postsecondary computer science students to program a simulation of a practical combinatorics problem. Students commented on whether and how this task helped them internalize the basic concepts of combinatorics. We aim to show how utilizing programming tasks may empower students with a deeper grasp of combinatorics.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"55 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140586063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring the use of ChatGPT in learning and instructing statistics and data analytics","authors":"Yixun Xing","doi":"10.1111/test.12367","DOIUrl":"https://doi.org/10.1111/test.12367","url":null,"abstract":"Generative artificial intelligence (AI) has shown the potential to reshape the world and redefine daily workflows. One specific instance of generative AI, ChatGPT, specializes in understanding natural language and generating human-like conversational text. Its free access, user-friendly interface, and instant feedback have propelled its popularity within and beyond education. Given its extensive knowledge of traditional statistics and contemporary data science, it can be considered for integration into modern statistics education. However, there have been ongoing questions and serious concerns regarding the accuracy and accountability of the responses generated by ChatGPT. This study explores ChatGPT's capabilities in addressing conceptual problems, implementing analytical techniques, and facilitating teaching while considering its disadvantages and ongoing development. With continued practice and deeper insights into this novel technology, its benefits can be cautiously leveraged in teaching and learning activities.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"3 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140586255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Salma Banu Nazeer Khan, Ayse Aysin Bilgin, Deborah Richards, Paul Formosa
{"title":"Educating students about the ethical principles underlying the interpretation of infographics","authors":"Salma Banu Nazeer Khan, Ayse Aysin Bilgin, Deborah Richards, Paul Formosa","doi":"10.1111/test.12362","DOIUrl":"https://doi.org/10.1111/test.12362","url":null,"abstract":"Infographics are visual storytelling techniques used to communicate complex information. However, infographics can be misleading if they are not created ethically. When universities teach how to create infographics, they often do so without emphasizing the ethical issues underlying infographics. To address this gap, we designed a study to educate statistics and data science students about the ethics of infographics by using Rest model's three stages: awareness, orientation, and intention. Students' awareness of the ethical issues underlying infographics was captured before and after sensitizing them to five ethical principles derived from the AI4People's framework applied to a data science context. The students were then exposed to scenarios with ethical dilemmas. Their identification of the ethical principles in these scenarios was measured. The results showed a significant increase in students' awareness of the ethical issues underpinning the interpretation of infographics, suggesting that ethical training of current users and future designers would be beneficial.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"50 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139373589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A gentle introduction to principal component analysis using tea-pots, dinosaurs, and pizza","authors":"Edoardo Saccenti","doi":"10.1111/test.12363","DOIUrl":"https://doi.org/10.1111/test.12363","url":null,"abstract":"Principal Component Analysis (PCA) is a powerful statistical technique for reducing the complexity of data and making patterns and relationships within the data more easily understandable. By using PCA, students can learn to identify the most important features of a data set, visualize relationships between variables, and make informed decisions based on the data. As such, PCA can be an effective tool to increase students data literacy by providing a visual and intuitive way to understand and work with data. This article outlines a teaching strategy to introduce and explain PCA using basic mathematics and statistics together with visual demonstrations.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"48 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139376609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Shilane, Nicole Di Crecchio, Nicole L. Lorenzetti
{"title":"Some pedagogical elements of computer programming for data science: A comparison of three approaches to teaching the R language","authors":"David Shilane, Nicole Di Crecchio, Nicole L. Lorenzetti","doi":"10.1111/test.12361","DOIUrl":"https://doi.org/10.1111/test.12361","url":null,"abstract":"Educational curricula in data analysis are increasingly fundamental to statistics, data science, and a wide range of disciplines. The educational literature comparing coding syntaxes for instruction in data analysis recommends utilizing a simple syntax for introductory coursework. However, there is limited prior work to assess the pedagogical elements of coding syntaxes. The study investigates the paradigms of the dplyr, data.table, and DTwrappers packages for R programming from a pedagogical perspective. We enumerate the pedagogical elements of computer programming that are inherent to utilizing each package, including the functions, operators, general knowledge, and specialized knowledge. The merits of each package are also considered in concert with other pedagogical goals, such as computational efficiency and extensions to future coursework. The pedagogical considerations of this study can help instructors make informed choices about their curriculum and how best to teach their selected methods.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"54 4","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Opting for open-source? A review of free statistical software programs","authors":"Melissa A. Shepherd, Elizabeth J. Richardson","doi":"10.1111/test.12360","DOIUrl":"https://doi.org/10.1111/test.12360","url":null,"abstract":"Statistical software is commonly used in undergraduate social sciences statistics courses. Due to the increase in online/hybrid courses and the cost of SPSS, instructors may wish to switch to another statistical software. We cover seven programs: Excel, Google Sheets, jamovi, JASP, PSPP, R, and SOFA. We compare programs using the following criteria: ease of download, quality of online instructions, availability of instructor resources, sophistication of analyses available, ease of use, operating system requirements, whether it uses point-and-click or code, and whether a VPAT is available. Adopting new course materials is a valuable part of instruction but time-consuming. Therefore, this review provides information about commonly available or free open-source programs so instructors can choose based on the needs of their students and/or institutions.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"53 9","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eva G. Makwakwa, David Mogari, Ugorji I. Ogbonnaya
{"title":"First‐year undergraduate students’ statistical problem‐solving skills","authors":"Eva G. Makwakwa, David Mogari, Ugorji I. Ogbonnaya","doi":"10.1111/test.12359","DOIUrl":"https://doi.org/10.1111/test.12359","url":null,"abstract":"Abstract This study investigated first‐year undergraduate statistics students’ statistical problem‐solving skills on the probability of the union of two events, conditional probability, binomial probability distribution, probabilities for x‐limits using the z‐distribution, x‐limit associated with a given probability for a normal distribution, estimating the y‐value using a regression equation, and hypothesis testing for a single population mean when a population standard deviation is unknown. The study was a descriptive case study and employed a mixed‐method research approach. Data were collected through content analysis of a statistics course examination script of 120 first‐year undergraduate students of statistics in an open distance‐learning university in South Africa. Polya's Model of Problem Solving was used as the framework of analysis. The study revealed that the students, in general, had poor statistical problem‐solving skills.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Applied biostatistics in clinical trials for 15‐year‐old pupils","authors":"David Lora","doi":"10.1111/test.12358","DOIUrl":"https://doi.org/10.1111/test.12358","url":null,"abstract":"Abstract It is important for young people to be aware of job profiles and activities in the professional world. Bringing the education system closer to the professional world is vital for them to make decisions about their academic and professional futures. Programs developed to connect 15‐year‐old students who in Spain are in year 4 of their Compulsory Secondary Education, and Research Support Units within the Health Research Institutes of the Hospitals and the Clinical Research Support Platforms of the Carlos III Institute of Health are a good opportunity to highlight the role of biostatistics in clinical trials. The aim of this article is to share the outcomes of and learnings from an interactive workshop for 15‐year‐old students on biostatistics and clinical trials conducted within the 4°ESO + Empresa program and directed by the Scientific Support Unit of the Health Research Institute of Hospital 12 de Octubre in Madrid, Spain.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136236818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Community of teaching practice informed by the discipline and scholarship","authors":"H. MacGillivray","doi":"10.1111/test.12357","DOIUrl":"https://doi.org/10.1111/test.12357","url":null,"abstract":"When I am asked where do the ideas or motivations for editorials come from, my reply is that they always arise in a recent happening. It can be something in recent submissions, or comments from reviewers, or some striking commonalities in themes across articles in an issue or with external events. This editorial reflects on the updating of the scope of the journal in this issue and also highlights the importance of visualization in the statistical and data sciences featured in an invited paper and its discussion.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48231159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}