Comprehensive refugee support requires additional data on variables related to mental health, job status, sanitation conditions, disease, unaccompanied minors, camp climate, and identification documents. This article looks at the data being collected and identifies gaps. The article highlights that while the humanitarian challenges are immense, the ability to respond well depends on the quality of the data collected.

Around 36 million people in the world are blind and an additional 217 million have moderate to severe vision impairment. In higher education, four percent of 54,204 undergraduates who participated in the 2022 American College Health Association survey reported to be blind or have low vision. Those students frequently do not have access to data visualizations we generally teach and use in postsecondary statistics and data science classes. The design of those visualizations is premised on implicit assumptions about the user’s visual ability. Making data visualizations accessible to blind and visually impaired (BVI) people would help improve equity in higher education and benefit them with data driven reasoning and communication. The Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report lists creating and interpreting graphical displays as one of the nine central goals for introductory statistics. We, as statistics and data science educators, researchers, or practitioners, should practice how to design accessible data visualizations, teach and use them in a way that is inclusive to the BVI community. This short article is a review of research and practices on teaching data visualizations for BVI students. I collected resources from related email threads in the Isolated Statisticians email list server and published articles over time. While this article pays particular attention to the BVI community, I acknowledge that other types of disabilities such as cognitive and motor disabilities may affect access to data visualizations as well.

This article highlights a highly biased interpretation of a clinical trial result in a press release on a primary efficacy endpoint. By identifying the issue, the article aims to encourage clinical trialists to adopt higher standards in interpreting trial results, thereby mitigating undue cynicism toward the industry.

Generative artificial intelligence (AI) tools, such as large language model-based chatbots (e.g., ChatGPT) and automated systems for generating text, images, videos, and 3D renderings, are increasingly finding applications in statistics and data science. The statistical community. In the spring of 2024, the American Statistical Association Committee on Data Science and AI conducted a survey to explore the use and planned adoption of generative AI tools among its members. The survey was administered online from May 13 to September 25, 2024, and garnered 177 responses. These insights provide a much-needed glimpse into how statisticians are engaging with generative AI technologies.

By juxtaposing statistical estimates which strive to be precise and accurate, with haiku that are more often purposely ambiguous or have several nuanced meanings, we raise the idea that the main similarity between statistics and haiku is that both are a reduction in dimensionality. Statistics takes many data points to a single or a set of numerical summaries or coefficients in a model, while haiku places “moments in time” into 3-line verse to convey gist which may be multisensory and filled with the poet's poignant feelings. To demonstrate this contiguity, we begin by introducing readers to pop-culture haiku and literary haiku with examples of both, then briefly refresh readers with the patterns of praxis in descriptive and inferential statistics. We provide examples of a quadratic regression to determine the optimal setting of a race car carburetor and of side-by-side boxplots for each subject in postural sway biomechanical data. Additional similarities include use of imagery (data visualization), hypothesis testing vs. third-line of haiku, pairing and contrast. In closing, we mention the current research use of generative language models to create haiku and question whether readers can distinguish between compositions by AI and humans.

A Zombie Research Result is one that despite having been killed with data long ago still walks the Earth, continuing to influence people who are unaware of its previous demise. This article examines the value of coaching in improving a candidate’s score on a college admissions test (e.g. the SAT or ACT). The authors claim we can determine how much of an advantage is conferred by having taken such a course of coaching with a straightforward study.

The challenges and opportunities researchers face when working with unstructured data are hardly new. This article defines unstructured data as data that is not organized according to pre-existing schemas or structures for the sake of statistical analysis. Unstructured data poses a unique challenge for researchers focused on police and policing. The article discusses a definition of unstructured data and two of the primary challenges faced when working with such data, namely information extraction and classification problems. Two case studies are used to illuminate the challenges.