GEOM90007 Chap.3 Data Types, Descriptive Statistics and Distribution

Data Types, Descriptive Statistics and Distribution

Define data type

Data Types, Descriptive Statistics and Distribution connects data type, distribution and descriptive statistic along an information-visualisation pipeline: data type, visual encoding, perception, task fit and evaluation.

The practical aim is to match the summary and display to the variable type and analytical comparison; the display is defensible only when each encoding choice can be traced back to the analytical question and the structure of the data.

Place data type at its correct stage: question, data specification, transformation, encoding, perception, tool or evaluation.

For data type, record the variables, measurement levels, granularity, missingness and comparison task wherever they apply before choosing or judging a chart.

Use distribution to make the next transformation, encoding, layout, interaction or evaluation step explicit.

When distribution involves marks and channels, state which values they represent and check whether scale, ordering, aggregation or filtering changes the apparent pattern.

Use descriptive statistic to test perceptual and task fit.

In Data Types, Descriptive Statistics and Distribution, ask whether viewers can make the required comparison accurately, whether uncertainty and exceptions remain visible, and whether colour, position, motion or interaction creates an avoidable accessibility or interpretation cost.

Trace distribution

For the application — match the summary and display to the variable type and analytical comparison — compare at least one alternative encoding against the same data and task.

Retain the design only if the descriptive statistic evaluation shows that it reveals the intended relationship without introducing a stronger distortion or hiding the evidence needed to challenge it.

Make a compact pipeline audit for data type: question or data field; transformation; mark and visual channel where applicable; intended task; evaluation evidence; and likely failure.

Locate distribution and descriptive statistic at their actual stages so a technically valid implementation is not mistaken for an effective display.

Test a changed distribution view. Hold the data and question stable, replace the channel most closely tied to distribution, and predict which comparison becomes easier or harder.

Evaluate the result with descriptive statistic, including uncertainty, accessibility and the possibility that an apparent pattern is produced by scale, binning, projection or interaction state.

Critique Data Types, Descriptive Statistics and Distribution in task order: state the question around data type, classify the data, describe the role of distribution, predict the perceptual judgement and report the descriptive statistic evaluation evidence.

Revise the first stage where descriptive statistic shows that the visual no longer supports the task instead of adding decoration to a structurally unsuitable chart.

Test with descriptive statistic

A complete response should make the task visible before the detail: identify what must be decided, define the relevant terms, connect the evidence to distribution, and use descriptive statistic to test the result.

The final sentence about descriptive statistic should answer the question actually asked rather than merely repeat the topic.

The controlling limit is specific: A compact statistic can hide multimodality, skew, outliers or subgroup differences.

Keep that descriptive statistic limit beside the worked example, because it separates a careful GEOM90007 answer from one that sounds confident but claims more than the task or evidence supports.

For revision, retrieve data type, distribution and descriptive statistic without notes, explain their relationship aloud, then complete a changed version of the application: match the summary and display to the variable type and analytical comparison.

Record the first failed distribution reasoning move and repair it before attempting another case.

What this chapter covers

• 01

  data type

• 02

  distribution

• 03

  descriptive statistic

• 04

  Applying data type

• 05

  Limits of distribution and descriptive statistic

Worked example: Data Types, Descriptive Statistics and Distribution

Key terms

data type

A classification of values by their meaningful operations and structure, such as categorical, ordinal, interval, ratio, temporal or spatial. Use this definition when the task is to match the summary and display to the variable type and analytical comparison.

distribution

The pattern of observed values across their range, including centre, spread, shape, clusters and unusual observations. Use this definition when the task is to match the summary and display to the variable type and analytical comparison.

descriptive statistic

A numerical summary of a sample or dataset such as a proportion, centre, spread or association measure. Use this definition when the task is to match the summary and display to the variable type and analytical comparison.

Data Types, Descriptive Statistics and Distribution FAQ

Assessment move

Reconstruct the relationship among data type, distribution and descriptive statistic; complete the chapter application without notes; then test the result against this limit: A compact statistic can hide multimodality, skew, outliers or subgroup differences.

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