Department of Statistics

Commentary on The Future of the Textbook.

The study of the brain is a compelling example of the power of convergent science. Over the last few decades, advances in neuroscience techniques and experimentation, as well as in data science tools to analyze the resulting data, have dramatically furthered our understanding of fundamental brain functions. Historically, it has been common for analytical approaches to have a considerable lag in development following the availability of new neuroscience techniques. However, this relationship has not simply been unidirectional, as there have been examples in which analytical developments have directly led to new scientific questions and experiments. Here we review how this interplay between neuroscience and data science advances has unfolded in the past and into the present, with a focus on electrophysiology and calcium imaging. Applying lessons learned from the past and present, we then discuss expected developments, challenges, and opportunities in the future. We end by providing recommendations on how to foster the necessary team science approach to continue the advancement of research at the intersection of neuroscience and data science, which we call neurodatascience, toward a sustainable future.

Open science advocates aim to address epistemic injustice and global power asymmetries in research infrastructures yet underestimate structural vulnerabilities and risks embedded in data sharing practices. Drawing on an ethnographic study of the GCRF South–South Migration Inequality and Development Hub’s (MIDEQ), we argue that even well-intentioned systems, such as the MIDEQ hub, do not reach these vulnerabilities. To capture these overlooked harms, we employ the concept of epistemic disadvantage, a category coextensive with, but separate from epistemic injustice. Epistemic disadvantage includes ethically justified data withholding and misappropriation within consensual systems, which is not captured by existing notions of extractivism as those focus on the monetisation of epistemic resources without regard for the loss incurred by the originating community. To better account for these harms, we draw on two recent epistemic attitudes: the Book of Truths (BoT), which sees scientific truths as emerging through peer review, and the Book of Conversations (BoC), which values data for the interactions and reflections it provokes. Even when epistemic values recognise knowledge as relational and situated, extractivist harms persist. We offer an ameliorated concept of extractivism to include subtler, systemic harms rooted in epistemic disadvantage.

Shiffrin et al. argue that scientific practice often produces illusions of understanding; situations in which familiar inferential tools generate misplaced confidence. We describe a related illusion that arises not from statistical misapplications, but from ordinary theory testing itself. Here we highlight a different source of epistemic uncertainty or overconfidence; one that emerges even when statistical tools are used correctly, effect sizes are precisely estimated, findings replicate without controversy, and heterogeneity is explicitly acknowledged within hierarchical models.

Previous research investigating exposure to per- and polyfluoroalkyl substances (PFAS) and their association with heart disease has found conflicting results, with recent studies reporting null or inverse associations. We estimated associations between serum PFAS and prevalent heart disease in a large, cross-sectional, multi-site study of adults residing in communities in the United States with historic PFAS drinking water contamination. Participants were recruited from communities in eight states (2019–2023), where they completed questionnaires and provided blood samples. PFAS concentrations were measured in serum. Four PFAS detected in > 70% of participants were modeled as continuous variables, and three less-frequently detected PFAS were modeled as binary variables (above/below the limit of detection). Logistic regression models estimated associations between each PFAS and prevalent heart disease based on self-report, adjusting for demographics, smoking and alcohol consumption, waist-to-hip ratio, estimated glomerular filtration rate (eGFR), and study site. Quantile g-computation models were used to estimate the joint effects of PFAS as a mixture. A total of 5,761 adult participants were included in the analysis, with a mean age of 56 years and 60.2% female. The prevalence of self-reported heart disease was 9.2%. Adjusted analyses showed no statistically significant associations between all PFAS and self-reported heart disease. Our mixture analysis revealed no association between PFAS as a mixture and prevalent self-reported heart disease (OR: 0.99, 95% CI: 0.95–1.03). Our findings suggest largely null associations between serum PFAS and self-reported heart disease.

Heat waves in California are occurring with increasing frequency, duration, and intensity, largely due to climate change. Furthermore, the burden of extreme heat disproportionately affects various communities both geographically and socioeconomically. As a result, understanding how heat is discussed on social media is critical for informing the design of public health interventions. This study involved a spatiotemporal analysis of heat-related tweets (N = 24,598) posted across California between April and October from 2016 to 2022. Poisson regression models were used to examine county-level counts of heat tweets, with population size included as an offset term in the model while logistic regression models estimated the probability that a heat tweet reflected coping or life impact (LI) themes. A different set of logistic regression models were fit to examine the effect of socio-demographic and housing variables on the likelihood of heat tweets expressing positive and negative sentiment, and on the likelihood of expressing coping or LIs. Explanatory variables were derived mostly from the American Community Survey and the Healthy Places Index. Sensitive housing (percentage of households living in an RV, van, or boat) and income predicted posting of heat tweets about coping, whereas income and percentage of active commuters predicted posting of heat tweets about LIs. Percentage of active commuters, tree canopy, and sensitive housing predicted negative heat tweets. Counties with greater proportion of Hispanic population in California also appeared vulnerable, showing a modest tendency toward greater LI and fewer coping-related heat tweets. These findings suggest that public health interventions should prioritize outreach to individuals living in sensitive housing, with an emphasis on indoor heat mitigation strategies. Overall, results demonstrate the value of social media heat discourse as a complementary tool for identifying vulnerable communities and informing targeted extreme heat awareness campaigns.

While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this paper, we provide twelve practical tips for AI-assisted coding that balance the capabilities of AI with the demands of scientific and methodological rigor. We address how AI can be leveraged strategically throughout the development cycle with four key themes: problem preparation and understanding, managing context and interaction, testing and validation, and code quality assurance and iterative improvement. These principles serve to emphasize maintaining human agency in coding decisions, establishing robust validation procedures, and preserving the domain expertise essential for methodologically sound research. These tips are intended to help researchers harness AI's transformative potential for faster software development while ensuring that their code meets the standards of reliability, reproducibility, and scientific validity that research integrity demands.

In this article, we examine the foundations of a rich and diverse literature in economics and derive the likelihood function of simultaneous equation models for discrete data as the invariant distribution of a suitably specified Markov process. This formulation offers a well-defined reduced form of the model and dispenses with the need for controversial recursivity requirements and ad hoc indeterminacy rules. The derivation resolves puzzling paradoxes highlighted in earlier work and shows that the likelihood is unique, proper, coherent, complete, and theoretically grounded in conditional distribution modeling – a framework that has yet to be popularized in economics. We note possible extensions and relevant links with other models, comment on computational issues, and implement the methodology in three empirical applications involving female labor force participation, the interactions between health and wealth, and the interplay between banks’ lending practices and their reliance on assistance from the lender of last resort during the Great Depression.

Acupuncture is an ancient practice that was developed within the framework of traditional Chinese medicine. While acupuncture has been recently proposed as a therapy for Alzheimer’s disease (AD), acupuncture effects are not well understood in terms of neural mechanisms. Here, we review and examine the studies that used AD mouse models and analyze the experiments where researchers administered electroacupuncture (EA) to AD mice to assess the potential therapeutic impact of acupuncture on disease pathology and cognitive function in controlled laboratory settings. We analyzed 29 relevant PubMed articles published between January 2014 and July 2025. Our results reveal that EA significantly reduces both amyloid-beta (Aβ) and phosphorylated tau (p-tau) levels and neuroinflammatory biomarkers, including molecular signatures for activated microglia and astrocytes in the brain. EA also enhances cognitive functions. While no study directly compared acupoint strategies, the indirect comparisons in our network analysis suggest that GV20 has potential as a therapeutic target for AD. Our present meta-analysis and review of literature add to the evidence of integrative health practices for acupuncture-based Alzheimer’s disease treatment.