arXiv subjects

Ilana M. Trumble

Publications and source records attributed to Ilana M. Trumble.

3 recordsLinked to original sources

arXiv2026

Closing the Gap: Can Novice Statistics and Data Science Students Collaborate as Effectively as an Expert?

The ASCCR (Attitude-Structure-Content-Communication-Relationship) framework was recently developed to teach collaboration skills to statisticians and data scientists. However, its effectiveness in real-world settings has not yet been systematically evaluated. To assess this, we evaluated novice undergraduate and graduate students' performances in initial collaboration meetings with real domain experts and compared them to an expert collaborator. Using video recordings, rubric scores, and domain expert feedback surveys, we found that novices performed surprisingly well compared to the expert. Specifically, novices scored nearly as well as the expert on the Attitude, Structure, and Relationship components of the ASCCR framework. Although novices did not initially perform as well on the Content or Communication aspects, they were able to close the gap. By the end of the collaboration projects, the novices had higher overall domain expert feedback scores than the expert. The primary implication of our study is that novices can become effective collaborators in a very short time. We discuss our findings' practical implications and provide recommendations for integrating the ASCCR framework into statistics and data science collaboration, consulting, and capstone courses.

stat.OT↗

arXiv2022

The Content of Statistics and Data Science Collaborations: the QQQ Framework

For today's applied statisticians and data scientists, collaboration is a reality. Statisticians (and data scientists) may collaborate with domain experts across academic fields, industry sectors, and governmental and non-governmental organizations. Thus, statisticians must develop skills and techniques for collaboration. To this end, we advance a framework called the Qualitative-Quantitative-Qualitative (QQQ, pronounced "Q-Q-Q") approach to systematize the content of statistical collaborations. The QQQ approach explicitly emphasizes the importance of the qualitative context of a project, as well as the qualitative interpretation of quantitative findings. We explain the QQQ approach and each of its components as applied to statistics and data science consultations and collaborations. We provide guidance for implementing each stage of the approach and present data evaluating the effectiveness of teaching the QQQ approach to beginning collaborators.

stat.AP↗

arXiv2019

Exact Power of the Rank-Sum Test for a Continuous Variable

Accurate power calculations are essential in small studies containing expensive experimental units or high-stakes exposures. Herein, exact power of the Wilcoxon Mann-Whitney rank-sum test of a continuous variable is formulated using a Monte Carlo approach and defining P(X < Y) = p as a measure of effect size, where X and Y denote random observations from two distributions hypothesized to be equal under the null. Effect size p fosters productive communications because researchers understand p = 0.5 is analogous to a fair coin toss, and p near 0 or 1 represents a large effect. This approach is feasible even without background data. Simulations were conducted comparing the exact power approach to existing approaches by Rosner & Glynn (2009), Shieh et al. (2006), Noether (1987), and O'Brien-Castelloe (2006). Approximations by Noether and O'Brien-Castelloe are shown to be inaccurate for small sample sizes. The Rosner & Glynn and Shieh et al. approaches performed well in many small sample scenarios, though both are restricted to location-shift alternatives and neither approach is theoretically justified for small samples. The exact method is recommended and available in the R package wmwpow. KEYWORDS: Mann-Whitney test, Monte Carlo simulation, non-parametric, power analysis, Wilcoxon rank-sum test

stat.ME↗