Statistically Sound Visual Data Analysis: Difference between revisions
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Previous work on visualizations and statistical analysis | Previous work on visualizations and statistical analysis | ||
* [http://pvis.github.io/introduction_to_vis/paper/Improving%20Bayesian%20Reasoning%20The%20Effects%20of%20Phrasing,%20Visualization,%20and%20Spatial%20Ability.pdf Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability ] | * Bayesian reasoning | ||
** [http://pvis.github.io/introduction_to_vis/paper/Improving%20Bayesian%20Reasoning%20The%20Effects%20of%20Phrasing,%20Visualization,%20and%20Spatial%20Ability.pdf Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability], Ottley et al., 2016 | |||
** [http://www.aviz.fr/bayes Assessing the effect of visualizations on bayesian reasoning through crowdsourcing], Micallef, Dragicevic, and Fekete, 2012 | |||
Revision as of 18:06, 25 February 2017
Overview
This page documents our research effort towards developing visual interfaces that help people perform statistically sound data analysis.
Proposals
- BIGDATA: F: Statistically Sound and Computationally Efficient Massive Data Analysis - Sample Complexity, Uniform Convergence, and False Discovery Rate
- Use cases
References
Vision and use case papers
- Towards Sustainable Insights (or why polygamy is bad for you): outlines a few examples of false discovery issues in existing visual data exploration (e.g. Vizdom) and visual recommendation tools (e.g. SeeDB)
Previous work on visualizations and statistical analysis
- Bayesian reasoning
- Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability, Ottley et al., 2016
- Assessing the effect of visualizations on bayesian reasoning through crowdsourcing, Micallef, Dragicevic, and Fekete, 2012