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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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Veridical data science.

Bin Yu1,2,3,4, Karl Kumbier5

  • 1Statistics Department, University of California, Berkeley, CA 94720; binyu@stat.berkeley.edu.

Proceedings of the National Academy of Sciences of the United States of America
|February 15, 2020
PubMed
Summary
This summary is machine-generated.

The predictability, computability, and stability (PCS) framework enhances data science with a workflow and documentation. It ensures responsible, reliable, and reproducible results through rigorous testing and transparent reporting.

Keywords:
computationdata sciencepredictionstability

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Area of Science:

  • Data Science
  • Machine Learning
  • Statistical Inference

Background:

  • Traditional data science workflows often lack robust mechanisms for ensuring reliability and transparency.
  • The impact of human judgment and computational choices on data analysis outcomes requires systematic evaluation.

Purpose of the Study:

  • To introduce the predictability, computability, and stability (PCS) framework for veridical data science.
  • To provide a structured workflow and documentation guidelines for responsible and reproducible data analysis.
  • To develop novel inference procedures for assessing the stability of data science results.

Main Methods:

  • The PCS framework integrates predictability, computability, and stability principles into a comprehensive workflow.
  • PCS inference procedures, including perturbation intervals and hypothesis testing, are developed to quantify result stability.
  • The framework is illustrated using neuroscience and genomics data, and validated through simulations of high-dimensional, sparse linear models.

Main Results:

  • PCS inference procedures demonstrate favorable performance compared to existing methods, particularly in ROC curve analysis for misspecified models.
  • The PCS framework effectively assesses the impact of human judgment and computational decisions on data analysis outcomes.
  • Simulations confirm the robustness of PCS in high-dimensional and sparse settings.

Conclusions:

  • The PCS framework offers a systematic approach to achieving veridical data science, enhancing result reliability and reproducibility.
  • PCS documentation, utilizing R Markdown or Jupyter Notebooks, promotes transparency and facilitates the replication of analyses.
  • The proposed methods and framework are valuable for advancing scientific inquiry across various data-intensive fields.