Statistics anxiety is recognized as a major challenge facing students at all academic levels. It particularly affects their performance in statistics and research methods courses. This study investigates changes in students’ statistics anxiety based on the perceived utility of learning resources and class attendance within an introductory statistics course. Through the application of social cognition theory, we also examine the role of previous performance in mathematics, perceived competence in mathematics, and statistical self-efficacy as contributors to statistics anxiety. A quasi-experimental approach was used with undergraduate students completing a survey at the start and end of an introductory statistics course. Paired sample t-tests indicated a significant reduction in statistics anxiety. Additionally, Pearson correlation points to negative correlations between posttest statistics anxiety scores, perceived competence in mathematics, and statistical self-efficacy. Using hierarchical regression, class attendance was found to predict posttest statistics anxiety, with the smaller classes having a greater impact on statistics anxiety. These findings highlight the need for further interrogation of the role of class size and classroom activities with explorations of how these may reduce students’ statistics anxieties.

Due to workforce demands and changing societal needs, ideally all university students, regardless of major, should have the opportunity to gain some level of data acumen in their university career. This article provides thoughtful methods of analysis for measuring how a higher education institution serves its students in providing opportunities to achieve three different levels of data acumen. Using one institution as a case study, the results show that overall students need more exposure to data analysis skills. The methods presented can be generalized and applied to other institutions worldwide thus contributing to the literature on how to assess students’ attainment of data acumen in their undergraduate experiences.

The rapid pace of digital transformation has created a pressing need for data literacy. While its importance is widely recognized, there is no clear consensus on the specific skills required. Current Data Literacy Education (DLE) approaches in data science and machine learning courses often focus on digital skills such as data preparation and analysis, overlooking essential foundational skills like creativity, agility, and collaboration. Moreover, as DLE spans beyond traditional STEM boundaries, there is a growing need for inclusive approaches that engage non-STEM learners and address students' diverse needs, interests, and goals. In response, we propose a practical framework for DLE in data science and machine learning courses that cultivates both digital and foundational skills within an inclusive learning environment. Our approach prioritizes active learning and coaching, leveraging our web application STATY to bridge the gap between theory and practice by engaging students in real-world data problem-solving. We illustrate its application through the master's-level course “Business Data Science – From Data to Forecasts and Decisions”, along with our experiences and lessons learned.

Statistics anxiety is a specific form of anxiety that arises when individuals encounter statistical concepts. It has been shown to affect educational outcomes and hinder academic performance. This study investigated the construct validity, measurement invariance, and discriminant validity of the Hungarian version of the Statistics Anxiety Rating Scale (STARS). Furthermore, we examined the relationship between statistics anxiety and performance in a statistics course and on the Cognitive Reflection Test that measures analytical thinking. The sample comprised 377 participants (85% female) with a mean age of 20.6 years, predominantly freshmen from Eötvös Loránd University and the University of Pécs. Our findings provide evidence for the reliability and validity of the Hungarian STARS, preserving its bifactor structure and subscales. All levels of measurement invariance were achieved, indicating that the Hungarian STARS reliably assesses statistics anxiety across universities and statistics course experience. Discriminant validity analysis revealed that statistics anxiety is distinct from other constructs. The global level of statistics anxiety had a weak negative correlation with performance and with analytical thinking. These results highlight the reliability and validity of the Hungarian STARS for measuring statistics anxiety in university settings.

This article recommends a refinement in teaching hypothesis testing in introductory, non-calculus-based statistics courses, focusing on how the null hypothesis is formulated in mean and proportion inference. The proposal is grounded in classroom experience at CSU Fullerton, where general education courses enroll liberal arts and natural science students in lecture–lab formats. Most mainstream textbooks at this level (e.g., Peck, Gould, De Veaux) present the null as an equality for one-sided tests. Instead, we suggest adopting the Neyman–Pearson framework, commonly used in mathematical statistics textbooks, which treats the null and alternative as complementary hypotheses, leading to an inequality in the null in one-sided tests. Framing the hypotheses in this way is more logical to students, allows fundamental hypothesis testing concepts such as Type I error and significance level to be taught more precisely, and helps students recognize the uncertainties inherent in hypothesis testing rather than viewing it as a purely algorithmic process. To support instructor implementation, we provide a classroom activity accompanied by an interactive Shiny app that allows students to explore Type I error probabilities empirically and visually. This pedagogical framework is consistent with GAISE recommendations to teach statistical thinking, focus on conceptual understanding, and use technology to explore ideas.

: This study explores high school students’ (n = 313) perceptions of and attitudes towards R programming in statistics and data science courses that use the CourseKata curriculum, which integrates R coding within an interactive statistics textbook. Specifically, we explore (a) shifts in confidence, positive sentiment, anxiety, perceived difficulty, and enjoyment from the beginning to the end of the course, (b) the predictive relationships between students’ attitudes toward R programming and their future interest in statistics and data science, and whether there are differential patterns by racially marginalized background (including Black, Latine, and other racially marginalized groups). Findings reveal positive shifts in students' confidence and sentiment, coupled with reductions in anxiety and perceived difficulty. For perceived difficulty, racially marginalized students showed less pronounced declines over time compared to non-marginalized students. Predictive analyses highlight that post-course confidence and enjoyment in learning R strongly predict future interest in statistics, particularly for racially marginalized students. These findings underscore the potential of integrated R programming curricula to foster positive attitudes toward coding, while also emphasizing the need to address equity gaps in students’ experiences.

This article provides a roadmap to explore the relationship between school quality and median single-family town-level home prices, school, and neighborhood characteristics using a unique dataset consisting of 152 towns in Greater Boston. Before the session, we ask students to read a well-known paper exploring the relationship between school quality and home prices. We discuss the paper in class and provide students with a new dataset containing similar information. As part of the class session, students consider how to use what they learned from a published paper to practice hypothesis formulation and testing with new data. We conduct simple and multiple regression analysis, interpret results, and estimate alternative model specifications. A goal of the exercise is to encourage students to create theory-driven regressions, rather than looking for a “right” answer. The topic and the dataset are appropriate for students who have taken introductory statistics and can be used in courses that include content related to educational achievement and financing, differences in socioeconomic status, and economic opportunities. Based on the topic’s broad appeal and accessibility, instructors could use the dataset to practice interpreting results of regression analyses without additional prework or context. We share the new dataset and accompanying material online.

When teaching entry-level statistical programming at universities, educators may wonder if they are alone in facing the challenge of spending valuable time explaining computer basics, such as file management. To better understand the knowledge gaps of students, we investigated the varying levels of computer literacy among university students enrolled in introductory statistical programming courses at a leading German university. We conducted and analyzed surveys among university students from three years, and we included similar questions in a general population survey to identify gaps in device usage, computer proficiency, and computational thinking. We refined existing instruments for capturing computer literacy and our refined instruments are open for reuse. Our findings reveal and discuss the heterogeneity in foundational computer skills in the context of students’ different majors, their varying secondary education, and the growing reliance on mobile devices.