Teaching Statistics最新文献

{"title":"The “p‐hacking‐is‐terrific” ocean ‐ A cartoon for teaching statistics","authors":"Ding-Ying Guo, Yue Ma","doi":"10.1111/test.12305","DOIUrl":"https://doi.org/10.1111/test.12305","url":null,"abstract":"P‐hacking is fishing for statistical significance through repeated testing on massive data. It would lead to spurious findings, misguide social practice and policy making, and thus should be avoided. Teaching about p‐hacking is important, yet challenging. Cartoons are effective edutainment tools to engage students in learning statistical concepts. We created a cartoon and discussed how to use it in teaching about p‐hacking by guiding students to think and answer a list of questions. This cartoon can be helpful with teaching both statistics courses and applied seminar courses in various other disciplines. Students are expected to gain a better understanding of multiple issues related to p‐hacking, including its occurrence due to repeated testing, the problems with using an arbitrary threshold for the P‐value and comparing statistical significance, the distinction between statistical vs scientific significance, the approach for interpreting testing results with a holistic view, and the strategies to avoid p‐hacking.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"68 - 72"},"PeriodicalIF":0.8,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48861810","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 case study of strategies for intentionally building course community to support diverse learners in an introductory statistics course","authors":"Laura Taylor, Kirsten Doehler, Ryne VanKrevelen, M. Weaver, Aaron Trocki","doi":"10.1111/test.12303","DOIUrl":"https://doi.org/10.1111/test.12303","url":null,"abstract":"This article presents a multi‐part initiative to support diverse learners by building class community and peer networks in an introductory statistics course. This was accomplished through multiple techniques, such as implementing icebreaker questions and using randomly assigned student working groups. The Socrative online software utilized regularly by instructors allowed students to be randomized into groups to collaboratively answer questions provided by the instructors. A multi‐part group project was also administered where students worked cooperatively to analyze swim race data from the 2016 Olympics. Students completed a pre‐semester survey in which they provided feedback on previous mathematics or statistics courses related to the level of course engagement, the benefit of group learning, and their ability to discuss course content during class. A post‐semester survey was administered to answer similar questions regarding the statistics course they were currently taking.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"48 - 58"},"PeriodicalIF":0.8,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43958574","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 comparison of limited vs unlimited attempts with online homework grades in introductory statistics courses","authors":"P. Stewart","doi":"10.1111/test.12304","DOIUrl":"https://doi.org/10.1111/test.12304","url":null,"abstract":"Online homework programs allow professors to preset how many attempts per homework problem each student is allowed to have. Some professors prefer to allow a limited number of extra attempts, and others prefer to allow students to have an unlimited number of extra attempts. Do these preferences lead to a difference in average homework grades? To study this question, seven statistics courses over the Fall 2020 and Spring 2021 semesters were analyzed. The Fall semester had a limited number of extra attempts for each homework problem, and the Spring semester had an unlimited number of extra attempts. The results of the study conclude that there is no statistically significant difference in average homework grades for most homework assignments. In any homework assignment with a statistically significant difference, there was a quantifiably small difference.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"73 - 81"},"PeriodicalIF":0.8,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49408400","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":"Characteristics of statistical literacy skills from the perspective of critical thinking","authors":"Shunya Koga","doi":"10.1111/test.12302","DOIUrl":"https://doi.org/10.1111/test.12302","url":null,"abstract":"Statistical literacy is generally defined as the ability to interpret and evaluate statistical information critically. It is regarded as a higher‐order literacy competence that includes critical thinking. While previous studies have illustrated the concept of statistical literacy, statistical literacy skills have not been sufficiently explored from the perspective of critical thinking research. To fill this gap, this study presents a framework for critical thinking skills in statistical literacy. The characteristics of general critical thinking skills were organized. Based on these characteristics, the researcher extracted sentences related to such skills from selected textbooks. As a result, eight aspects of critical thinking skills in statistical literacy were identified.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"59 - 67"},"PeriodicalIF":0.8,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42755057","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":"Statistical edutainment: A means to vary how we teach summary statistics","authors":"D. Pearl, L. Lesser","doi":"10.1111/test.12301","DOIUrl":"https://doi.org/10.1111/test.12301","url":null,"abstract":"Edutainment fun items can engage students in discussing and learning key concepts about descriptive statistics.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"82 - 89"},"PeriodicalIF":0.8,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42828916","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 descriptive study of syphilis testing in Manitoba, Canada, 2015-2019.","authors":"Souradet Shaw, Pierre Plourde, Penny Klassen, Derek Stein","doi":"10.14745/ccdr.v48i23a07","DOIUrl":"10.14745/ccdr.v48i23a07","url":null,"abstract":"<p><strong>Background: </strong>In 2018, Manitoba had the highest reported rate of infectious syphilis in Canada, at over three times the national average. Infectious syphilis in Manitoba is centred on young, marginalized heterosexual couples in Winnipeg's inner-city. Subsequently, a public health crisis involving congenital syphilis emerged in Manitoba, just prior to the coronavirus disease 2019 pandemic. Testing and screening (in the case of pregnancy) for syphilis is thought to be an effective measure to reduce the incidence of syphilis and its sequelae. The aim of this study is to describe syphilis testing practices in the general population and amongst pregnant women, during a period of shifting syphilis epidemiology.</p><p><strong>Methods: </strong>We used population-based syphilis testing data from Cadham Provincial Laboratory (Winnipeg, Manitoba) for 2015 to 2019. Directly age-standardized rates are reported, and Poisson regression used to model the determinants of testing rates. Rates of prenatal screening are also reported.</p><p><strong>Results: </strong>From 2015 to 2019, a total of 386,350 individuals were tested for syphilis. The rate increased annually, from 462 per 10,000 population in 2015 to 704 per 100,000 in 2019, while the female-to-male ratio decreased from 1.8 to 1.6. Prior to 2019, the majority of pregnant women (approximately 60%) were screened once, during the first trimester; however, 2019 saw more women having more than two tests during the course of their pregnancy.</p><p><strong>Conclusion: </strong>An overall increase in the number of individuals tested was observed, reflecting the increased rate of syphilis in Manitoba. Prenatal screening patterns shifted in 2019, likely in response to rising congenital syphilis numbers.</p>","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"26 1","pages":"95-101"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85136153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International Association for Statistical Education Announcements","authors":"","doi":"10.1111/test.12296","DOIUrl":"https://doi.org/10.1111/test.12296","url":null,"abstract":"","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45665741","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":"C Oswald George prize announcement 2021","authors":"H. MacGillivray","doi":"10.1111/test.12295","DOIUrl":"https://doi.org/10.1111/test.12295","url":null,"abstract":"The article entitled “Interrogating a measurement conjecture to introduce the concept of statistical association in upper elementary education” by Mairéad Hourigan and Aisling Leavy has been awarded the C. Oswald George prize for 2021 This paper describes an investigation involving active collecting of personal data and exploring patterns in the data, to introduce students aged 11-12 years to their first experiences of thinking about association between continuous variables. The teaching exercise was also part of preservice training, with two teacher educators working alongside five preservice primary teachers to design and implement the statistical investigation to introduce statistical association, and support future development. The lesson was taught by two preservice teachers and small group work was facilitated by the other preservice teachers and the teacher educators. The investigation was into jump height and jump length, involving careful consideration of experiment, measurement and data collection conditions. The students were then asked to attempt graphical representation of their data, leading to discussion and questions of what is relationship and how to represent it. On then being shown a scatterplot for the first time, the students moved through questions of reading the data, reading between the data and reading beyond the data. Amongst other observations, it was noted that young students tend to case-oriented views to tackle new concepts but also “possess the potential to understand the concepts of statistical association as well as the communicative function of scatter plots”. The lesson provides authentic learning experiences for both students and preservice teachers, and combines excellent statistical pedagogy and good practice in teaching statistical thinking with sound grounding in the scholarly literature of educational research. The clear exposition of this combined but authentic approach provides interesting reading for all, at any level, who aim to teach statistics from an enquiryoriented student-based approach, as it provides valuable insight into students' reactions to first meeting of this key statistical concept, its graphical representation, attendant ideas of variation and estimation, with concomitant learning for preservice primary teachers. By concluding with thoughtful reflections and suggestions for improvement of this lesson for both students and teachers, the paper reinforces its demonstration of combining scholarly work, sound pedagogy, good practice in teaching statistics, and authentic understanding of what is important in statistics to produce an interesting and thought-provoking learning experience and paper. Congratulations to the authors for their excellent paper.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41411176","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":"Teaching Statistics Trust prize for 2021 special issue","authors":"H. MacGillivray","doi":"10.1111/test.12298","DOIUrl":"https://doi.org/10.1111/test.12298","url":null,"abstract":"Teaching Statistics are happy to announce that the Teaching Statistics Trust has awarded a prize for the best paper in the 2021 special issue, Teaching Data Science and Statistics: foundation and introductory, to Anna Fergusson and Chris Wild for their paper On traversing the data landscape: Introducing APIs to data-science students. Statistics and data science and their teaching are intrinsically linked. This is seen not only in the increasing inclusion of technology in teaching statistics, but also in the data and contexts considered, and the broadening of statistical issues, explorations, presentations, and discussions at introductory levels, whether school, undergraduate or postgraduate/workplace in other disciplines. The intent of the special issue is to provide impetus and inspiration to all readers and authors in furthering this progress, and to celebrate the new subtitle of the journal, in the increasing awareness of what data science is, and how statistics and data science work together in tackling real and complex datasets and problems involving complex data. Data science is much more than a new set of tools it opens doors to whole new ways of thinking about information, explanation, and action, and the special issue demonstrates what an extraordinarily rich field this is and just how much challenge and opportunity there are that could, and should, be considered by the statistical and data science community. Amongst the excellent papers illustrating a wide variety of approaches and offering some very rich examples for teaching in this emerging space, the special issue editors, after much debate, chose the winning paper because of the importance of harvesting the vast amounts of data now available combined with authentic student engagement in enquiry-based learning in a fun and universally appealing context. The pedagogic approach is an excellent demonstration of the long-time advocacy of leading statisticians and statistical educators of students learning of technical tools and statistical thinking via graduated needs arising in the tackling of a real data investigation that piques student curiosity and exploration. The proposal, using APIs, is unique and cutting edge, but is explained in an extremely clear way. It centers on the importance of the data gathering phase in data science (at least when it comes to data scraping), and mastery of this skill not only empowers students, but teaches them that the internet really is just organized data. However such approaches cannot succeed without careful scaffolding, preparation and deep understanding of student needs in learning about data. Students move from immersion in a search activity (for photos) to URL hacking and GUIdriven tools, to thinking of variables and then to API’s. Graphical explorations are then encouraged to at least partially discuss some of the questions that have arisen during a student’s personal journey in the investigation. The approach is simple, well written, directl","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42223476","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":"Thanks to all","authors":"H. MacGillivray","doi":"10.1111/test.12299","DOIUrl":"https://doi.org/10.1111/test.12299","url":null,"abstract":"Thank you to Teaching Statistics Trust members, editorial board members, reviewers, authors and readers of this unique and important journal. For everyone involved with teaching, whether at school, tertiary or in the workplace, the past two years have been challenging. I particularly extend deep gratitude to all our reviewers who put so much thought and effort into this very important professional work which often does not receive the appreciation from authorities that it should. The effects and influences of SARS-CoV-2 (Covid 19) on society and on teaching, as well as the increasing work to further learning in statistics and data science, have been reflected in the admirable efforts, initiatives and thinking of authors. I particularly wish to thank everyone involved in the special issue published in 2021, Teaching Data Science and Statistics: foundation and introductory. The success of the excellent work by authors and reviewers is evidenced by the many downloads and enquiries. My coeditors Jim Ridgway and Rob Gould were outstanding in all aspects of their work. It is not easy to develop and implement good practice in teaching statistics and data science, nor is it easy to write well and meet scholarly writing standards about it. But these efforts are some of the most important in education, particularly at foundation and introductory levels across all disciplines, and the challenges are also opportunities to further this work. Best wishes for 2022 and keep contributing to inspiring, teaching and writing about best practices in our International Journal for Statistics and Data Science Teaching.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46975525","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}