Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

<jats:title>Abstract</jats:title><jats:p>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 <jats:italic>P</jats:italic>‐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.</jats:p>

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

<jats:title>Abstract</jats:title><jats:p>This article discusses and contrasts the measures of association introduced by Pearson, Spearman, and Kendall, as these are the three most commonly used in practice and also the ones primarily covered in introductory statistics courses. Emphasis is placed on concepts pertaining to the measurement of the level of association between two variables, the calculation of the coefficients, and the interpretation of the calculated values. In particular, we demonstrate how Spearman's rho and Kendall's tau can be expressed in terms of Pearson's correlation coefficient based on transformed data. Important concepts and potential pitfalls are illustrated using numerical examples.</jats:p>

<jats:title>Abstract</jats:title><jats:p>In this article, we offer a teaching lesson on combinatorics and binary outcomes that utilizes real‐world data. The focus of the lesson is to teach students how to analyze the effects of the National Basketball Association's (NBA) 2003 decision to extend the first round of its postseason from a best‐of‐five series of games to a best‐of‐seven series using combinatorics and ideas about binary outcomes. Students conjecture how much longer series will make less certain the outcomes of these series and then use the 27 years of first‐round series results we provide to evaluate their conjectures on how series results have changed since 2003. After finishing this lesson, students will have a firmer grasp on applying combinatorics and binary outcomes to real‐world situations. This lesson is compatible with both traditional and remote classes and can be extended to other sports, making it a lesson for all academic seasons.</jats:p>