Graphical Statistics


Graphical statistics shape how data is explored, interpreted, and acted upon. The choice of visual and summary methods influences what patterns are noticed, how confidently conclusions are drawn, and whether decisions are supported by evidence. Effective use of graphical statistics therefore plays an important role in supporting robust analysis.

Technique Overview

Graphical Statistics

Graphical Statistics Definition

Graphical statistics refer to the use of visual and summary methods to explore, assess, and interpret data, including charts, plots, and simple descriptive measures. Unlike presentation-focused data visualisation, graphical statistics are analytical tools used to understand distributions, variation, relationships, and anomalies before conclusions are drawn. They support reasoning about data quality, uncertainty, and appropriateness of conclusions, and enable judgment on whether statistical claims are supported by evidence (Tukey, 1977; Cleveland and McGill, 1984; Spiegelhalter, 2019).

Graphical Statistics Description *

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Business Evidence

Strengths, weaknesses and examples of Graphical Statistics *

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Business Application

Implementation, success factors and measures of Graphical Statistics *

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Professional Tools

Graphical Statistics videos and downloads *

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Further Reading

Graphical Statistics web and print resources *

Graphical Statistics references (4 of up to 20) *

  • Tukey, J.W., 1977. Exploratory data analysis. Reading/Addison-Wesley.
  • Cleveland, W.S. and McGill, R. (1984). Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 79(387), pp.531–554. doi:https://doi.org/10.1080/01621459.1984.10478080
  • Few, S., 2004. Show me the numbers. Analytics Pres, 2.
  • Spiegelhalter, D., 2019. The art of statistics: Learning from data. Penguin UK.

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Related Concept: Collating and Formatting Data

 Before any analysis can happen, data needs to be collated from correct sources and formatted so it follows clear organisational standards. Research shows that inconsistent structures and formats make data harder to combine, process and trust (Jagadish et al., 2014). Collating and formatting ensure the dataset is clean, consistent and ready for use.