Statistically Sound Visual Data Analysis: Difference between revisions
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* BIGDATA: F: Statistically Sound and Computationally Efficient Massive Data Analysis - Sample Complexity, Uniform Convergence, and False Discovery Rate | * BIGDATA: F: Statistically Sound and Computationally Efficient Massive Data Analysis - Sample Complexity, Uniform Convergence, and False Discovery Rate | ||
** Use cases | ** [[Use cases]] | ||
== References == | == References == | ||
Revision as of 22:06, 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
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
- Visual estimation of coefficients of statistical models
- Judging correlation from scatterplots and parallel coordinate plots, Li, Martens, and van Wijk, 2008. This paper reports experimental results on how well people can assess correlation under varying sample size and visualization methods (scatter plots v.s. parallel coordinate plots). The results suggest that the accuracy of statistical judgements varies across visualization designs, motivating careful analysis and experimentation when designing visualizations to facilitate statistical data analysis.