Teaching Statistics最新文献

{"title":"The how of data","authors":"H. MacGillivray","doi":"10.1111/test.12329","DOIUrl":"https://doi.org/10.1111/test.12329","url":null,"abstract":"For many decades, professional statisticians and statistics educators have emphasized the central importance of identifying, taking account of, and reporting the 5 W's of data—What, Why, When, Where, and by Whom. If data are to be collected or accessed, we can add How—how can we obtain the data we need or want. The word “How” used broadly, can also encompass much of the 5 W's, as the What and Why are needed to understand How the necessary or desired data can be obtained, or were obtained. That these are all integral to statistics and statistics investigations has also been emphasized but it can never be highlighted enough that they should be at the heart of teaching statistics, no matter to whom or at what level. It can be a delight for teachers to discover this; I will always remember the excitement of senior school teachers learning this 30 years ago in hands-on professional development workshops— “You mean this is all part of statistics, not just preliminaries to statistics? Wow!”. Unfortunately, learning from discipline and/or teaching frontlines does not necessarily penetrate the citadel of educational authority. The question of the Who, the What, the How, and the How much of teaching statistics in education faculties, whether for future teachers or future research (where the multiple t-test tyranny appears to continue unchecked), is open for a different discussion. As the eras of big data and data science gradually grew and then exploded, the 5 W's and the How of data in teaching have “of course” become even more important and have received renewed attention, as commented by many authors, including in the 2021 special issue of Teaching Statistics. But as Shatz [6] reminds us in this issue, we should avoid saying “of course” and be ever mindful of the perpetual need to both explain and illuminate what statistics is, including that the central roles of the 5 W's and the How of data are of critical importance in real data science. In this issue, Lasater et al [2] highlight that “two critical learning elements now are working with complex publically-available datasets and choice and use of appropriate visualization in investigating multivariable data.” In [2], “These are the focus of the lab activity described here, set in an important social context.” Expansion to complex, large publically-available datasets and technologically intensive procedures does not mean relegation of other types of datasets or data collections. It just means the big tent of statistics and statistics teaching got even bigger. Collecting data, observing data, experimental design, and surveys still have major roles to play across all of statistics and its applications, and in teaching. But no matter what type or size of dataset, and no matter what the teaching context, without knowing, taking account of, and reporting on the 5 W's and the How of the data, analysis and interpretation may be compromised. Three articles in this issue provide excellent illustrations of this ","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"1 - 3"},"PeriodicalIF":0.8,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48270929","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":"Learning statistics with interactive pictures using R Shiny: Generally preferred, but not generally advantageous","authors":"F. Zhao, Lena Schützler, O. Christ, R. Gaschler","doi":"10.1111/test.12324","DOIUrl":"https://doi.org/10.1111/test.12324","url":null,"abstract":"Constructing interactive web apps has become more accessible for instructors, for example, by using the R package Shiny. Here we explored learners' preferences and the efficiency of interactive simulations versus static pictures in acquiring statistics knowledge of Cohen's d and standard normal distribution. Results revealed that students' spontaneous interaction with pictures was infrequent (pilot study, N = 26). While prompts (Exp. 1, N = 152) effectively ensured the manipulation of simulations, student exposure to interactive simulations led to longer learning times though similar test performance compared with student exposure to static pictures. Multiple interactive representations led to lower test performance than single interactive and static representations (Exp. 2, N = 117). Though no advantage was gained regarding learning outcomes, participants preferred the interactive variant (Exp. 3, N = 119). Taken together, this study demonstrates that the superiority of interactive pictures cannot be assumed to hold in general. Further work should evaluate how mental model construction can be effectively scaffolded by interactive simulations.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"106 - 124"},"PeriodicalIF":0.8,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42731779","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: An experimental way to teach for good measure","authors":"Dennis K. Pearl, L. Lesser","doi":"10.1111/test.12323","DOIUrl":"https://doi.org/10.1111/test.12323","url":null,"abstract":"Concepts of experimentation and measurement are explored using statistics educational fun items and illustrated by sharing our process in conducting an experiment on cartoon captions.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"45 - 56"},"PeriodicalIF":0.8,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42891811","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":"Statistics and clinical trials: It's all about the design","authors":"Sheuli Porkess","doi":"10.1111/test.12325","DOIUrl":"https://doi.org/10.1111/test.12325","url":null,"abstract":"This article explains the basic ideas and practical challenges in clinical trials of new medicines to show the practical application of statistics in the real‐world. The article explores the key considerations for the objectives and design of clinical trials and how these relate to the statistical investigation process. The article also includes examples of practical exercises for students.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"51 10","pages":"27 - 35"},"PeriodicalIF":0.8,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41304976","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":"Bare bones, or a rich feast? Taking care with context in a data rich world","authors":"S. Finch, I. Gordon","doi":"10.1111/test.12322","DOIUrl":"https://doi.org/10.1111/test.12322","url":null,"abstract":"Providing a rich context has become a sine qua non of principled teaching of applied statistical thinking. With increasing opportunities to access secondary data, there should be increasing opportunity to work with rich context. We review the contextual information provided in 41 data sets suitable for introductory tertiary statistics teaching, available in the R “datasets” package, and investigate the source information for four data sets. We find failure to describe and retain important contextual information, including aspects that raise questions about the credibility of the data for statistical inference. The sanitization of data reduces the opportunities for learning meaningful lessons in statistical thinking and the real‐world application of statistics. We advocate for teachers and users of such data to be curious about the provenance and context, and for the curators and distributors to examine, where possible, the primary sources, to accurately preserve the context and optimize pedagogical opportunities.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"13 - 4"},"PeriodicalIF":0.8,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41845798","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":"Lessons from the famous 17th‐century paradox of the Chevalier de Méré","authors":"José Daniel López‐Barrientos, Eliud Silva, Enrique Lemus-Rodríguez","doi":"10.1111/test.12321","DOIUrl":"https://doi.org/10.1111/test.12321","url":null,"abstract":"We take advantage of a combinatorial misconception and the famous paradox of the Chevalier de Méré to present the multiplication rule for independent events; the principle of inclusion and exclusion in the presence of disjoint events; the median of a discrete‐type random variable, and a confidence interval for a large sample. Moreover, we pay tribute to our original bibliographic sources by providing two computational tools to facilitate the students' insights on these topics.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"36 - 44"},"PeriodicalIF":0.8,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43963666","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":"The curse of knowledge when teaching statistics","authors":"Itamar Shatz","doi":"10.1111/test.12320","DOIUrl":"https://doi.org/10.1111/test.12320","url":null,"abstract":"When teaching statistics, educators sometimes overestimate their students' knowledge and abilities. This is due to the curse of knowledge, a cognitive bias that causes people—especially experts—to overestimate how likely others are to know and understand the same things as them. This can lead to various issues, including struggling to communicate with students, and making students feel less comfortable in the classroom. To address this, educators should first identify situations where this bias can affect their teaching. In doing so, they should consider relevant risk factors, and potentially also solicit feedback from relevant individuals. Then, educators can reduce this bias and its impact on their teaching by using techniques such as keeping the curse of knowledge and their audience in mind, assessing students' knowledge, assuming lack of knowledge unless there is strong evidence to the contrary, and avoiding saying that things are obvious.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"22 - 26"},"PeriodicalIF":0.8,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46729850","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 a complex gender wage gap dataset: an introductory activity in identifying issues and data visualization","authors":"Robert S. Lasater, Anny-Claude Joseph, Kevin Cummiskey","doi":"10.1111/test.12319","DOIUrl":"https://doi.org/10.1111/test.12319","url":null,"abstract":"In this paper, we provide instructors with an approach for a classroom activity for students in an introductory data science or statistics course who have little or no statistical programming experience. We designed this activity to help students improve their statistical literacy while exploring a social justice problem‐the gender wage gap. To minimize the challenges of developing statistical literacy in students who lack programming skills, we developed a web‐based data visualization application that does not require users to have any prior programming knowledge. The data in this visualization application comes from the March 2018 Current Population Uniform Extracts detailed by the Center for Economic Policy Research. Students can use the visualization application to create tables and plots to explore data on factors such as earnings and gender. Instructors can also use the application for other wage‐related variables, such as race, occupation and family size.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"14 - 21"},"PeriodicalIF":0.8,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47891591","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":"What do you, the reader, want?","authors":"H. MacGillivray","doi":"10.1111/test.12316","DOIUrl":"https://doi.org/10.1111/test.12316","url":null,"abstract":"Recently, I was fascinated to see the original trust deed of 1978 setting up the Teaching Statistics Trust, which stated that, as part of the purpose “for the public benefit”, the Trust's aim was to set up a Journal to be “devoted to the dissemination of educative information about statistics and the teaching of statistics...”. The aim inside the front cover of the first issue in 1979 [1], included words that have been retained to this day within the current Aims and Scope. In particular, the words in bold in the following current statement were in the original Aim: “Teaching Statistics seeks to inform, enlighten, stimulate, guide, correct, inspire, entertain and encourage.” Similarly to the original that “The emphasis of the articles is on teaching and the classroom”, the key messages in the current Aims and Scope of Teaching Statistics are that this journal is “......intended for all those who teach statistics and data science ....... The emphasis is on good practice in teaching statistics, statistical thinking and data science in any context.......”. The initial support for the journal [1] was provided by four professional organisations, the International Statistical Institute (ISI), the Royal Statistical Society (RSS), the Institute of Statisticians (merged with the RSS in 1993), and the Applied Probability Trust (APT). The support and involvement of these professional organisations are indicative of the focus on education by the whole statistical community in the 1960's and 1970's. When the new United Nations took over many of the previous government-oriented responsibilities of the International Statistical Institute (ISI), the ISI took on more general professional roles, including setting up its Education Committee in 1948. Although the initial educational focus was on training in official statistics, particularly in developing countries, as described in [2,9], interest rapidly grew and broadened to university teaching, both for future statisticians and across disciplines, and then to schools. The chairpersons of the ISI Education Committee, and the topics of the Committee's Roundtable Meetings in the 1960's and 1970's (see [9]) reflect the intertwining work of leaders in the rapidly evolving and broadening discipline of the statistical sciences. Another indication was the establishment of the APT in 1964 at the University of Sheffield by Joseph Gani, Professor of Statistics at Sheffield 1965-1974, to publish the Journal of Applied Probability as an outlet for work on wide-ranging real problems, such as in genetics, epidemics, finance; applied probability is an integral and essential constituent of the broad tent of the statistical sciences. Statistics education and teaching must always evolve and broaden to reflect the growth and developments in our wideranging and increasingly vital discipline of the sciences of statistics, data and chance. The intertwining of statistical education developments can be seen in [1,2,9]. In the UK, the sett","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48396987","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":"Probabilities, odds, and vigorish","authors":"Joseph G. Eisenhauer","doi":"10.1111/test.12318","DOIUrl":"https://doi.org/10.1111/test.12318","url":null,"abstract":"This paper uses actual data on horse racing to illustrate probabilities, odds, and expected values, and offers cautionary remarks about applying textbook formulas to gambling on real‐world sporting events.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"126 - 132"},"PeriodicalIF":0.8,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44249510","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}