15 new articles on statistical workflow!

2026-05-29 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Mathematics & Computational Sciences, Research Methodology & Innovation · Depth: Expert, quick

Summary

A special issue titled "Statistical Workflow for the Philosophical Transactions of the Royal Society" has been released, co-edited by Andrew Gelman, Aki Vehtari, Lizzie Tipton, and Richard McElreath. This collection addresses the critical gap where much of the effective workflow used by top statistics and data analytics practitioners remains tacit knowledge, often overlooked in standard textbooks and research. The issue comprises 15 articles from leading statisticians and researchers, exploring diverse workflow motivations and details across various disciplines. Topics include Bayesian workflow, unsupervised machine learning for scientific discovery, PCS workflow for veridical data science, simulations in statistical workflows, automatic finite-sample robustness metrics (Parts I & II), building a Backdrop of Meaning in Magnitude (BoMM), preliminary data analysis for meta-analysis, a four-step simulation-based workflow for ecological analysis, scientific workflow in experimental economics, hidden processes in cognitive developmental psychology, reproducible workflow for online AI in digital health, model checks for Bayesian estimation in health coverage, closing the gap between statistical and scientific workflows in ecology, and machine learning workflows in climate modeling. All papers are freely available.

Key takeaway

For research scientists or data scientists seeking to improve methodological rigor, this special issue offers invaluable insights into diverse statistical workflows. You should explore the freely available articles to uncover documented best practices. Focus on areas like Bayesian analysis, simulation-based modeling, or robustness testing. Integrating these explicit workflow details can enhance your research's reproducibility and validity. This moves beyond tacit knowledge to more transparent scientific processes.

Key insights

Best statistical and data analytics practices often rely on undocumented, tacit workflows.

Principles

• Workflow details are crucial for scientific rigor.
• Diverse disciplines share workflow challenges.
• Explicitly documenting workflow improves practice.

Method

The collection explores various workflows, including a four-step simulation-based workflow for ecological analysis and a PCS workflow for veridical data science.

In practice

• Explore Bayesian workflow techniques.
• Apply robustness metrics to data analysis.
• Design reproducible AI workflows.

Topics

• Statistical Workflow
• Bayesian Workflow
• Data Analytics
• Reproducibility
• Machine Learning Workflows
• Scientific Discovery

Best for: AI Scientist, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.