Designing Graphics Requires Useful Experimental Testing Frameworks and Graphics Derived From Empirical Results

papers
Author

Susan VanderPlas

Published

July 30, 2021

Keywords

npr, user testing, visualization, modeling

Abstract

Hullman and Gelman (2021, this issue) have provided a very thorough discussion of the premise that interactive exploratory data analysis requires a theoretical framework for graphical inference to effectively support the analyst and counter any tendencies towards assuming all results are real and not just due to sample variability. Unfortunately, the theoretical framework for model checking during exploratory and confirmatory data analysis proposed in this paper is just another conceptual and theoretical framework that is difficult to test or falsify as presented.

Citation

[1] S. VanderPlas. “Designing Graphics Requires Useful Experimental Testing Frameworks and Graphics Derived From Empirical Results”. In: Harvard Data Science Review 3.3 (Jul. 30, 2021). DOI: https://doi.org/10.1162/99608f92.7d099fd0.

@article{vanderplasDesigningGraphicsRequires2021,
  author = {Susan VanderPlas},
  title = {Designing {Graphics} {Requires} {Useful} {Experimental} {Testing} {Frameworks} and {Graphics} {Derived} {From} {Empirical} {Results}},
  journal = {Harvard Data Science Review},
  publisher = {MIT Press},
  volume = {3},
  number = {3},
  year = {2021},
  month = {7},
  doi = {https://doi.org/10.1162/99608f92.7d099fd0},
}