Introduction to Graphs, Data Visualization, and Interpretation

A set of vocabulary flashcards covering basic graph terminology, data classification, visual encodings, and principles of data visualization design derived from week two lecture and tutorial materials.

Dataset

The entire collection of data presented in a table or figure.

Variable

A specific category or attribute in a dataset, such as name, age, or height.

Value

A specific piece of information within a variable, such as a specific person's name or a specific number of children.

Data Point

A full row of data that includes a value for all variables in the dataset.

Numerical Data

Data that represents an amount of something.

Discrete Data

A type of numerical data that is counted and not measured, such as the number of pets or the result of a roll of a die.

Continuous Data

A type of numerical data that is measured rather than counted, where there are meaningful midpoints between numbers, such as height ($157.49\,\text{cm}$) or speed ($101.11\,\text{km/hr}$).

Categorical Data

Data where items are assigned to labeled categories rather than measuring an amount.

Nominal Data

A type of categorical data where the categories have no inherent order and can be rearranged without changing the meaning, such as countries or postcodes used as labels.

Ordinal Data

A type of categorical data where the labels have a specific, inherent order, such as race medals ($\text{Gold, Silver, Bronze}$) or academic grades ($\text{Pass, Credit, Distinction, High Distinction}$).

Content

The internal part of a graph that actively represents the data, such as lines or shaded areas.

Scaffolding

The container and context of a graph that helps with interpretation, including axes, labels, grid lines, captions, and titles.

Visual Encoding

A property of a graph (such as size, color, or position) that varies according to the underlying data.

Length or Height

A visual encoding where the size of a bar in a chart varies in proportion to the data, such as population bars where $327 \times 4 = 1308$.

Position

A visual encoding where the location of a data point along an axis (usually the x-axis or y-axis) tells the viewer about the data value.

Area

A visual encoding where the size of a bubble or a segment in a pie chart is proportional to the data it represents.

Angle

A visual encoding used in pie charts or line slopes; for example, if a category is $60\%$ of the data, its angle in a circle would be $216^{\circ}$ ($60\% \text{ of } 360^{\circ}$).

Colour Hue

A visual encoding using different colors (e.g., red vs. gray) to distinguish between different categories or datasets.

Colour Shade

A visual encoding where the intensity or shade of a color represents a range of values, such as temperature variations.

Shape

A visual encoding where different symbols (e.g., circles vs. crosses) are used to denote different groups or types of data.

Width or Thickness

A visual encoding where the thickness of line branches or tentacles is proportional to the number they represent.

Sharper Minds

A package of free, evidence-based preventive health and self-care strategy modules developed at UQ for students.

Principle of Proportional Ink

The design principle stating that when a shaded region represents data, the area of that region must be directly proportional to the data value (e.g., a bar representing $70\%$ should take up $70\%$ of the available space).

Consistent Bin Sizes

The principle that when data is grouped into categories, each 'bin' should be of equal size (e.g., age groups of $0-9, 10-19, 20-29$) to ensure fair interpretation.

Motherese in Dolphins

The finding that bottlenose dolphin mothers use a higher pitch (maximum frequency z-score) in their signature whistles when with their calves compared to when they are alone.

Data in the Wild Assignment

A coursework task requiring students to find a problematic graph from the real world published since $02/2025$ and write a letter to the publisher explaining the issues and providing an improved version.