For a number of years, Stat has been publishing a series of lay abstracts on Stats & Data Science Views, where authors provide an accessible summary of their articles in non-technical language.

The collection of lay abstracts is listed below.

• Garcia, A, Lee, J, Balasubramanian, V, et al. The development of a mobile app-focused deduplication strategy for the Apple Heart Study that informs recommendations for future digital trials. Stat. 2022;e470. Accepted Author Manuscript. https://doi.org/10.1002/sta4.470 Lay Abstract

• Kabaila, P., & Farchione, D. (2022). Confidence intervals that utilize sparsity. Stat, 11( 1), e434. https://doi.org/10.1002/sta4.434 Lay Abstract

• Boschi, T., Chiaromonte, F., Secchi, P., & Li, B. (2021). Covariance-based low-dimensional registration for function-on-function regression. Stat, 10( 1), e404. https://doi.org/10.1002/sta4.404 Lay Abstract

• Tsao, M. (2021). A constrained minimum method for model selection. Stat, 10( 1), e387. https://doi.org/10.1002/sta4.387 Lay Abstract

• Ebner, B, Liebenberg, SC. On a new test of fit to the beta distribution. Stat. 2021; 10:e341. https://doi.org/10.1002/sta4.341 (OPEN ACCESS) Lay Abstract

• Bertin, AM, Baumer, BS. Creating optimal conditions for reproducible data analysis in R with ‘fertile’. Stat. 2021; 10:e332. https://doi.org/10.1002/sta4.332 Lay Abstract

• Wagner, AB, Hill, EL Ryan, SE, et al. Social distancing merely stabilized COVID‐19 in the United States. Stat. 2020; 9:e302. https://doi.org/10.1002/sta4.302 Lay Abstract
• Sesia, M, Candès, E. A comparison of some conformal quantile regression methods. Stat. 2020; 9:e261. https://doi.org/10.1002/sta4.261 Lay Abstract
• Liu, Y, Zheng, C. Deep latent variable models for generating knockoffs. Stat. 2019; 8:e260. https://doi.org/10.1002/sta4.260 Lay Abstract

For accepted manuscripts, if you feel that your article will have broad appeal beyond the readership of Stat, you may wish to consider providing a “lay abstract” to appear on this site. 

This abstract or commentary should be written in the third person, be substantially less technical than the formal abstract, and be at least 200 words. It should explain the importance of your work in a broader context, appealing to a non-specialist statistical audience. We may use this information to highlight your research on Stats & Data Science Views and our social media networks. When ready, you may email your lay abstract to Stephen Raywood (sraywood@wiley.com).