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Looks Good To Me: Visualizations As Sanity Checks
IEEE Transactions on Visualization and Computer Graphics
|August 24, 2018
Summary
Visualizations can hide data flaws like missing values or extra modes, even when they seem to summarize data well. Unoptimized design parameters obscure these critical issues, impacting data quality analysis.
Area of Science:
- Data Visualization
- Exploratory Data Analysis
- Data Quality Assessment
Background:
- Visualizations reveal patterns missed by summary statistics, crucial for identifying data quality issues.
- Design parameters (e.g., bin size, opacity) significantly impact visual appearance but are rarely optimized for flaw detection.
Purpose of the Study:
- To investigate how visualization design parameters can obscure data flaws.
- To propose best practices for data quality tasks using visualizations of distributions.
Main Methods:
- Crowdsourced study based on the Algebraic Visualization Design framework.
- Analysis of how data flaws impact visual features in common visualization types.
Main Results:
- Common visualizations can mask significant data flaws (e.g., missing data, extraneous modes) despite appearing to summarize data.
- Uncritical or adversarial setting of design parameters exacerbates the obscuring of data flaws.
Conclusions:
- Standard visualization practices may fail to reveal critical data quality issues.
- Optimizing design parameters and adopting new best practices are essential for effective data quality assessment through visualization.
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