Data visualisation Flashcards
(17 cards)
Why visualize data?
Enhances understanding of datasets
Historical example: Florence Nightingale (1858) used visualization to expose poor conditions in military hospital
Anscombe’s Quartet
4 datasets with identical summary stats but very different plots
Introduced in 1973 to demonstrate the importance of visual representation in data.
Datasaurus Dozen (Matejka & Fitzmaurice, 2017)
12 datasets with identical descriptive stats but visually different plots
Includes one dataset shaped like a dinosaur.
Purpose of data visualisation in analysis
-Check assumptions before stats test
-Understanding variable relationships
Helps in understanding variable relationships.
Purpose of data visualization in writing/publication
-Communicate findings clearly
-Guide interpretation of data
Guides interpretation of data.
Types of graphs for checking assumptions
-Histogram
-Boxplot
Types of graphs for summarizing descriptives
-Bar chart
-Clustered bar chart
Type of graph for graphing relationships
Scatterplots (help examine associations and assumptions)
Key components of APA format for graphs
-Title and numbered figure caption
-Clearly labelled axes (with units)
-Include notes for missing data or clarifications
What makes a good graph according to Tufte (2001) and APA (2021)?
-Clear images
-Proper units and axis labels
-Label all elements
-Encourage data-driven interpretation
-Avoid ‘chartjunk’
‘Chartjunk’
-Unnecessary design elements that can obscure the data’s message
Examples of bad visualizations include misleading COVID graphs and political misrepresentation.
When are tables best used?
Displaying large amounts of precise numerical data
When are figures better than tables?
For spotting trends or relationships
Tables in APA format
-Clear labels
-Titles
-Explanatory notes
Chart used when summarising data
A table or bar chart
Chart used when highlighting trends/patterns
Scatterplots or line graphs
Other ways to visualise data
-Visual vocab tools
-Creative maps
