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Students need "critical data literacy" skills to help make sense of the multitude of information available to them, especially as it relates to high-stakes issues of social justice. The authors describe two curriculum modules they developed--one on income equality, one on immigration--that help students learn to analyze data in order to shed light on complex social issues and evaluate claims about those issues.

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 (precourse 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 postcourse 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.

"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. for this article are available online. [ABSTRACT FROM AUTHOR]

An interview with Soma Roy, an associate professor in the Department of Statistics at Cal Poly in San Luis Obispo, California, is presented. She discusses her classroom teaching experience at the Ohio State University as well as the improvement of her teaching style. She also mentions that she is currently teaching introductory statistics.