Title: Introduction: data visualisation
Course: UGQuants (SSPS) > Section: Data Visualisation

Introduction to module 11.

What is Visualisation? Why Visualisation Matters.

Recent years have seen an explosion in the use of visual techniques to present quantitative data.  Maps, graphs and info graphics are common journalistic tools, while the availability of powerful graphing functions in statistical software means that visual methods can now be used by everyone to present their research findings in a way that is accessible to all.

The objective of visualisation is to identify, and present to a wider audience, patterns present within complex datasets.  In essence, the graphs we produce can be seen as story-telling tools, extracting a narrative from the numbers we have. Many visualisations can be seen as analogous to traditional (numeric) statistical techniques, for instance scatterplots are equivalent to Parson’s correlations, multiple box plots a visual representation of the one-way ANOVA.

When used well, visualisation techniques can not only help to present simple findings from quantitative analysis, but also provide an intuitive method for understanding complex, multivariate, relationships.  The intuitive, and accessible, appearance of visualisations means they can provide powerful tools for presenting a persuasive argument.  Compared to tables of figures, visualisations can often be appealing, and convincing, to non specialist audiences.

In addition to providing a mechanism for describing patterns within data, visualisation can act as a useful prelude to further analysis, for instance checking the distribution of variables before including them in a regression model.  Many visualisations are descriptive, although it is possible to include some indications of statistical significance to give a more inferential appearance.

However, a word of warning is in order: pretty graphs cannot rescue poor analysis, and indeed, done badly visualisation can hide meaningful relationships, or lead to erroneous conclusions! Effective visualisation should be built on identifying the simplest presentation which accurately presents the patterns present in our data.