We present an efficient method of calculating exact confidence intervals for the hypergeometric parameter representing the number of “successes,” or “special items,” in the population. The method inverts minimum-width acceptance intervals after shifting them to make their endpoints nondecreasing while preserving their level. The resulting set of confidence intervals achieves minimum possible average size, and even in comparison with confidence sets not required to be intervals it attains the minimum possible cardinality most of the time, and always within 1. The method compares favorably with existing methods not only in the size of the intervals but also in the time required to compute them. The available R package hyperMCI implements the proposed method.

We introduce a pair of Shiny web applications that allow users to visualize random forest prediction intervals alongside those produced by linear regression models. The apps are designed to help undergraduate students deepen their understanding of the role that assumptions play in statistical modeling by comparing and contrasting intervals produced by regression models with those produced by more flexible algorithmic techniques. We describe the mechanics of each approach, illustrate the features of the apps, provide examples highlighting the insights students can gain through their use, and discuss our experience implementing them in an undergraduate class. We argue that, contrary to their reputation as a black box, random forests can be used as a spotlight, for educational purposes, illuminating the role of assumptions in regression models and their impact on the shape, width, and coverage rates of prediction intervals.

"Comment on “On the Power of the F-test for Hypotheses in a Linear Model” by Griffiths and Hill (2022)." The American Statistician, 76(3), pp. 310–311

As a result of the increased emphasis on mis- and over-use of p-values in scientific research and the rise in popularity of Bayesian statistics, Bayesian education is becoming more important at the undergraduate level. With the advances in computing tools, Bayesian statistics is also becoming more accessible for undergraduates. This study focuses on analyzing Bayesian courses for undergraduates. We explored whether an undergraduate Bayesian course is offered in our sample of 152 high-ranking research universities and liberal arts colleges. For each identified Bayesian course, we examined how it fits into the institution’s undergraduate curricula, such as majors and prerequisites. Through a series of course syllabi analyses, we explored the topics covered and their popularity in these courses, and the adopted teaching and learning tools, such as software. This article presents our findings on the current practices of teaching full Bayesian courses at the undergraduate level. Based on our findings, we provide recommendations for programs that may consider offering Bayesian courses to their students.

ABSTRACT When releasing record-level data containing sensitive information to the public, the data disseminator is responsible for protecting the privacy of every record in the dataset, simultaneously preserving important features of the data for users’ analyses. These goals can be achieved by data synthesis, where confidential data are replaced with synthetic data that are simulated based on statistical models estimated on the confidential data. In this article, we present a data synthesis case study, where synthetic values of price and the number of available days in a sample of the New York Airbnb Open Data are created for privacy protection. One sensitive variable, the number of available days of an Airbnb listing, has a large amount of zero-valued records and also truncated at the two ends. We propose a zero-inflated truncated Poisson regression model for its synthesis. We use a sequential synthesis approach to further synthesize the sensitive price variable. The resulting synthetic data are evaluated for its utility preservation and privacy protection, the latter in the form of disclosure risks. Furthermore, we propose methods to investigate how uncertainties in intruder’s knowledge would influence the identification disclosure risks of the synthetic data. In particular, we explore several realistic scenarios of uncertainties in intruder’s knowledge of available information and evaluate their impacts on the resulting identification disclosure risks.

An important step for any causal inference study design is understanding the distribution of the subjects in terms of measured baseline covariates. However, not all baseline variation is equally important. We propose a set of visualizations that reduce the space of measured covariates into two components of baseline variation important to the design of an observational causal inference study: a propensity score summarizing baseline variation associated with treatment assignment and a prognostic score summarizing baseline variation associated with the untreated potential outcome. These assignment-control plots and variations thereof visualize study design tradeoffs and illustrate core methodological concepts in causal inference. As a practical demonstration, we apply assignment-control plots to a hypothetical study of cardiothoracic surgery. To demonstrate how these plots can be used to illustrate nuanced concepts, we use them to visualize unmeasured confounding and to consider the relationship between propensity scores and instrumental variables. While the family of visualization tools for studies of causality is relatively sparse, simple visual tools can be an asset to education, application, and methods development.

We adapt the social game “Two truths and a lie” to a classroom setting to give an activity that introduces principles of statistical measurement, uncertainty, prediction, and calibration, while giving students an opportunity to meet each other. We discuss how this activity can be used in a range of different statistics courses.

Recent literature has shown that statistically significant results are often not replicated because the “p-value < 0.05” publication rule results in a high false positive rate (FPR) or false discovery rate (FDR) in some scientific communities. While recommendations to address the phenomenon vary, many amount to incorporating additional study summary information, such as prior null hypothesis odds and/or effect sizes, in some way. This article demonstrates that a statistic called the local false discovery rate (lfdr), which incorporates this information, is a sufficient summary for addressing false positive rates. Specifically, it is shown that lfdr-values among published results are sufficient for estimating the community-wide FDR for any well-defined publication policy, and that lfdr-values are sufficient for defining policies for community-wide FDR control. It is also demonstrated that, though p-values can be useful for computing an lfdr, they alone are not sufficient for addressing the community-wide FDR. Data from the recent replication study are used to compare publication policies and illustrate the FDR estimator.

Byron J. Gajewskia*, Jo A. Wicka & Truman J. Milling Jrba Department of Biostatistics & Data Science, The University of Kansas Medical Center, Kansas City, KSb Seton Dell Medical School Stroke Institute, Austin, TX