"Simulation-based inference" is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In this article, we highlight for teachers some implications of different simulation strategies when analyzing two variables. In particular, does it matter whether the simulation models random sampling or random assignment? We present examples from comparing two means and simple linear regression, highlighting the impact on the standard deviation of the null distribution. We also highlight some possible extensions that simulation-based inference easily allows. Supplementary materials for this article are available online.

While many instructors are aware of the Literary Digest 1936 poll as an example of biased sampling methods, this article details potential further explorations for the Digest's 1924-1936 quadrennial U.S. presidential election polls. Potential activities range from lessons in data acquisition, cleaning, and validation, to basic data literacy and visualization skills, to exploring one or more methods of adjustment to account for bias based on information collected at that time. Students can also compare how those methods would have performed. One option could be to give introductory students a first look at the idea of "sampling adjustment" and how this principle can be used to account for difficulties in modern polling, but the context is rich in other opportunities that can be discussed at various times in the course or in more advanced sampling courses. Supplementary materials for this article are available online.

Simulation-based inference methods continue to gain in popularity across grade levels and institutions. Despite their popularity, there continues to be a dearth of assessment data available demonstrating their efficacy across diverse student populations. We have collected comprehensive data about student performance in high school (that is, secondary) statistics courses using a variety of different curricular and pedagogical approaches, including, but not limited to, simulation-based inference. In this article, we summarize and discuss a comparison of pre-course and post-course conceptual understanding in aggregate and on topic specific scales between high school statistics courses using simulation-based curricular materials and those using the traditional (or consensus) curriculum. Our findings suggest higher achievable gains on a statistics concept inventory, overall and on subscales, for students using simulation-based curricular materials, compared to those using consensus curricula.

The Strengthening Data Literacy across the Curriculum (SDLC) project has been developing and researching curriculum modules to build interest and skills in data science among U.S. high school students from historically marginalized groups. SDLC modules are centered on investigations of social justice questions using large-scale social science data and the Common Online Data Analysis Platform (CODAP). This paper examines the extent to which students show increased interest in statistics and data analysis, and stronger understanding of core statistical concepts and multivariable thinking, after completing a three-week SDLC module. This paper also discusses ways in which a social justice focus may contribute to students’ interests in and understanding of data analysis.

To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality in the United States. Designed as a multi-week set of applied data investigations, the module supports student analyses of income inequality using U.S. Census Bureau microdata and the online data analysis tool the Common Online Data Analysis Platform (CODAP). Pre- and post-module data show that use of this module was associated with statistically significant growth in students' understanding of fundamental data concepts and individual interests in statistics and data analysis, with small to moderate effect sizes. Student survey responses and interview data from students and teachers suggest that the topic of income inequality, features within CODAP, the use of person-level data, and opportunities to engage in multivariable thinking helped to support critical data literacy and its foundations among participating students. We describe our definitions of data literacy and critical data literacy and discuss curriculum strategies to develop them.

Learning standards for biology courses have called for increasing statistics content. Little is known, however, about biology students’ attitudes towards statistics content and what students actually learn about statistics in these courses. This study aims to uncover changes in attitudes and content knowledge in statistics for students in biology courses. One hundred thirty-four introductory biology students across five different instructors participated in a pre-post study of statistical thinking and attitudes toward statistics. Students performed better on the statistics conceptual inventory at the end of a biology course compared to the beginning. Student attitudes showed no change. These preliminary results suggest the potential importance for laying a conceptual foundation in statistics prior to taking biology courses with little formal statistical instruction.

Typically research in education is done through observational studies, often focusing on end-of-course grades or exam scores. We explored the use of smaller-scale, focused experiments performed in genuine classroom settings. Six instructors across two institutions implemented a series of experiments focused on implementation decisions in teaching a simulation-based inference curriculum. We found the experiments to be feasible and very valuable in helping to understand whether, and why, some pedagogical methods are better than others. We offer suggestions for larger scale implementation.

Recognizing that mathematics teachers are facing increasing demands in the teaching of probability and statistics (stochastics) at school level, we are interested in analyzing current pre-service teachers’ dispositions about content knowledge and attitudes towards the teaching and learning of stochastics. We implemented a quantitative study for a sample of 269 pre-service Chilean mathematics teachers to determine their understanding of stochastics content, their attitudes towards stochastics and its teaching, and whether these are related. We found weak associations overall, but stronger for some components. We conclude with recommendations based on these results to improve the Chilean teachers’ preparation process (pre-service) and advice that could guide the professional development of teachers (in-service).

Explore data science learning modules that promote students’ interests and practices in statistics and large-scale data analysis using social justice topics that relate to Black and Latinx students’ lives both inside and outside of school.

Promoting positive student attitudes towards stochastics has become a core goal of statistics education reform, and we argue that this starts with the teachers during their teacher preparation program. Building on previous work assessing teachers’ attitudes, we focus on how pre-service teacher attitudes vary across different dimensions, and how these patterns can inform teacher training. We present results from assessing attitudes towards stochastics and its teaching for a sample of 269 pre-service Chilean mathematics teachers across three topics: descriptive statistics, probability, and statistical inference. Using a quantitative approach, and considering attitudes towards content and pedagogy, we focus on describing the main attitudinal differences among these three areas. In general, we found positive attitude towards the content and its teaching in all three areas, but with differences among them, primarily in the area of statistical inference. We end with some proposals aimed at improving teacher preparation, focusing on helping pre-service teachers understand the utility and overarching process of statistical investigations.

Chronic hepatitis B, a condition associated with severe complications, disproportionately affects Asian Americans and Pacific Islanders in the United States. Increasing testing among this population is critical for improving health outcomes. This study compares different types of video narratives that use storytelling techniques to an informational video (control), to examine whether narratives are associated with higher hepatitis B beliefs scores and video rating outcomes. A sample of Asian American and Pacific Islander adults ( N = 600) completed an online survey where they viewed one of four video conditions, three of which included storytelling techniques and one with informational content. Results indicated that parental stories received significantly higher perceived effectiveness ratings ( M = 3.88, SD = 0.61) than the older adult personal stories ( M = 3.62, SD = 0.74), F(3, 596) = 3.795, p = .010. Parental stories also had significantly higher perceived severity scores ( M = 3.83, SD = 0.69) compared to the young adult stories ( M = 3.73, SD = 0.74) and the informational videos ( M = 3.83, SD = 0.69), F(3, 596) = 7.72, p < .001. The informational videos ( M = 4.10, SD = 0.65) received significantly higher message credibility ratings than the older adult personal stories ( M = 3.84, SD = 0.70), F(3, 596) = 4.71, p = .003. Follow-up tests using Bonferroni correction revealed that parental stories ( M = 3.98, SD = 0.64) and young adult personal stories ( M = 3.934, SD = 0.76) scored significantly higher on speaker ratings than the older adult personal stories ( M = 3.698, SD = 0.77). Results suggest that storytelling has the potential for connecting with a specific audience in an emotional way that is perceived well overall. Future research should examine the long-term impact of hepatitis B personal story videos and whether the addition of facts or statistics to videos would improve outcomes.

Introductory biology courses typically incorporate data collection and analysis into laboratory assignments, and therefore teach some basic statistics. However, these curricula are generally developed without input from statisticians and are taught by instructors, often graduate students, with a range of experience using statistics but no training in statistics education. Given the importance of statistical thinking and other quantitative approaches in biology, the Statistical Thinking in Undergraduate Biology (STUB) network was established to facilitate coordination of best practices and assessment of statistical thinking in introductory biology students. As an example of the network’s benefits, we present a case study involving redesign of laboratory activities in an Introduction to Organismal Form and Function course. Changes include alignment of language with that used in statistics courses, more thorough instruction about experimental design and statistical tests, and use of an online simulation tool shown to improve understanding of statistical inference in statistics courses. These changes are supported by statistics faculty participating in the training of biology teaching assistants. An assessment in progress compares conceptual understanding and attitudes toward statistics before and after the redesigned lab activity; the assessment tool is being used throughout the STUB network, providing comparable data across institutions. This kind of cross‐disciplinary collaboration has the potential to improve statistical thinking in undergraduate biology students, better preparing them for advanced coursework and for careers in modern biology.Support or Funding InformationNSF Research Coordination Network‐Undergraduate Biology Education (RCN‐UBE)

Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an introductory or second course in statistics. After adjusting for the change in track length and track condition, winning speed has an interesting nonlinear trend, with one notable outlier. Student investigations can range from validating the phrase "the most exciting two minutes in sports" to predicting the winning speed of next year's race using parallel polynomial models.

"Simulation-based inference" has been advocated with the potential of improving student understanding of statistical inference, as well as the statistical investigative process as a whole. One justification is that the approach calls for improved pedagogy (i.e., more active learning and use of technology to explore statistical ideas). The “flipped classroom,” where students spend class time working on explorations and out-of-class time reading the text and watching videos, has also been gaining popularity in recent years. But can a simulation-based inference (SBI) course be flipped? In this study, the same instructor taught an SBI course as a flipped class and in a more traditional format during the same term. We explore differences in student attitudes, conceptual understanding, and course performance between the two sections.

The recent simulation-based inference (SBI) movement in algebra-based introductory statistics courses (Stat 101) has provided preliminary evidence of improved student conceptual understanding and retention. However, little is known about whether these positive effects are preferentially distributed across types of students entering the course. We consider how two metrics of Stat 101 student preparation (pre-course performance on concept inventory and math ACT score) may or may not be associated with end of course student performance on conceptual inventories. Students across all preparation levels tended to show improvement in Stat 101, but more improvement was observed across all student preparation levels in early versions of a SBI course. Furthermore, students' gains tended to be similar regardless of whether students entered the course with more preparation or less. Recent data on a sample of students using a current version of an SBI course showed similar results, though direct comparison with non-SBI students was not possible. Overall, our analysis provides additional evidence that SBI curricula are effective at improving students' conceptual understanding of statistical ideas post-course regardless student preparation. Further work is needed to better understand nuances of student improvement based on other student demographics, prior coursework, as well as instructor and institutional variables.