Podcast
Questions and Answers
What do good visualizations aim to do?
What do good visualizations aim to do?
Statistical graphs are more vulnerable to distortion than words.
Statistical graphs are more vulnerable to distortion than words.
False
Define the Lie Factor in graphical representation.
Define the Lie Factor in graphical representation.
Lie Factor is the ratio of the size of effect in a graphic to the size of effect in the data, indicating distortion.
The larger share of ink on a graphic should present statistical data information to maximize the _________.
The larger share of ink on a graphic should present statistical data information to maximize the _________.
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Match the principles for good quantitative graphs with their descriptions:
Match the principles for good quantitative graphs with their descriptions:
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What guiding principle states that the physical extent of data representation in graphs should be directly proportional to the numerical quantity?
What guiding principle states that the physical extent of data representation in graphs should be directly proportional to the numerical quantity?
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What is the Lie Factor formula for determining distortion in graphics?
What is the Lie Factor formula for determining distortion in graphics?
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Data out of context provides no comparative ________.
Data out of context provides no comparative ________.
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Maintaining integrity in quantitative graphs involves avoiding distortion and eliminating lie-factors.
Maintaining integrity in quantitative graphs involves avoiding distortion and eliminating lie-factors.
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Match the following principles for good quantitative graphs with their descriptions:
Match the following principles for good quantitative graphs with their descriptions:
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Study Notes
Principles of Excellent Visualization
- Informative visualizations provide insight into complex data by exploiting human perceptual pattern matching and recognition capability
- A good visualization should show the data, induce the viewer to think about the substance rather than the methodology or technology of graphic production, and avoid distorting what the data have to say
- A good visualization should also make large data sets coherent, encourage the eye to compare different pieces of data, and reveal the data at several levels of detail
Data and Model Dependency
- A graphical representation can only be as good as the data that is displayed
- An unreasonable model results in an absurd visualization
Principles for Good Quantitative Graphs
- Maintain integrity
- Prevent design variation
- Show data in context
- Maximize data-ink ratio
- Maximize data density
Integrity of Data and Design
- Statistical graphs can be distorted, but graphics are no worse or more vulnerable to distortion than words
- Distortion is introduced in data graphs if the visual representation is inconsistent with numerical values
- Eliminate exaggeration and overdecoration
The Lie Factor
- Violations of the principle of matching graphical extent to represented quantity lead to a Lie Factor
- Lie Factor = size of effect in graphic / size of effect in data
- Lie factors outside [0.95, 1.05] indicate substantial distortion
- Factors of 2 to 5 are not uncommon
Design Variation
- Design variation refers to the change of representation in form or scale over the surface of the graphic
- Leads to ambiguity and deception
- Leads to large lie factors
- Graphic should show data variation, not design variation
Misleading Design
- Visual and statistical tricks geared towards exaggeration of budget growth
- Example: New York Times, February 1, p.IV-7, 1976
Misleading Statistics
- Statistical lapses include ignoring population increase and monetary inflation
- Corrected graph, of the same data, shows steady budget from 1970
Context
- Data out of context provides no comparative scale
Data-Ink
- The larger share of ink on a graphic should present statistical data information
- Data-ink ratio = data-ink used / total ink used
- Maximize the data-ink ratio
- Erase non-data-ink
- Erase redundant data-ink
Clarity of Visual Presentation
- Visualization can quickly become unclear and cluttered
- Guiding principles:
- Use few and non-obtrusive graphical elements
- Make good use of the graphical space
Avoid Graphical Clutter
- Decorations and patterns may cause disturbing effects
- Avoid Moiré vibrations
Data Density
- Amount of quantitative data is given by matrix of observed variables
- Data density relates the size of the graphic to the amount of data displayed
- Data density = number of matrix entries / area of graphic
Principles of Excellent Visualization
- Informative visualizations provide insight into complex data by exploiting human perceptual pattern matching and recognition capability
- A good visualization should show the data, induce the viewer to think about the substance rather than the methodology or technology of graphic production, and avoid distorting what the data have to say
- A good visualization should also make large data sets coherent, encourage the eye to compare different pieces of data, and reveal the data at several levels of detail
Data and Model Dependency
- A graphical representation can only be as good as the data that is displayed
- An unreasonable model results in an absurd visualization
Principles for Good Quantitative Graphs
- Maintain integrity
- Prevent design variation
- Show data in context
- Maximize data-ink ratio
- Maximize data density
Integrity of Data and Design
- Statistical graphs can be distorted, but graphics are no worse or more vulnerable to distortion than words
- Distortion is introduced in data graphs if the visual representation is inconsistent with numerical values
- Eliminate exaggeration and overdecoration
The Lie Factor
- Violations of the principle of matching graphical extent to represented quantity lead to a Lie Factor
- Lie Factor = size of effect in graphic / size of effect in data
- Lie factors outside [0.95, 1.05] indicate substantial distortion
- Factors of 2 to 5 are not uncommon
Design Variation
- Design variation refers to the change of representation in form or scale over the surface of the graphic
- Leads to ambiguity and deception
- Leads to large lie factors
- Graphic should show data variation, not design variation
Misleading Design
- Visual and statistical tricks geared towards exaggeration of budget growth
- Example: New York Times, February 1, p.IV-7, 1976
Misleading Statistics
- Statistical lapses include ignoring population increase and monetary inflation
- Corrected graph, of the same data, shows steady budget from 1970
Context
- Data out of context provides no comparative scale
Data-Ink
- The larger share of ink on a graphic should present statistical data information
- Data-ink ratio = data-ink used / total ink used
- Maximize the data-ink ratio
- Erase non-data-ink
- Erase redundant data-ink
Clarity of Visual Presentation
- Visualization can quickly become unclear and cluttered
- Guiding principles:
- Use few and non-obtrusive graphical elements
- Make good use of the graphical space
Avoid Graphical Clutter
- Decorations and patterns may cause disturbing effects
- Avoid Moiré vibrations
Data Density
- Amount of quantitative data is given by matrix of observed variables
- Data density relates the size of the graphic to the amount of data displayed
- Data density = number of matrix entries / area of graphic
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Description
Learn about the principles of creating effective and informative visualizations that provide insight into complex data and avoid distorting the message.
