Graphs are to visualize quantitative data and relationship between variables using a system of coordinates. They are powerful in getting the message across, but the same data can be displayed in many ways, with a variety of visual effects. Examples include line graphs, histograms, and bar graphs. These graphical tools help us to see magnitude, trends, differences and similarities in the data. They are a key aspect in scientific communication for any audience. There is no general advice about when it is appropriate to use a graph rather than a table. Graphs offer the opportunity to show more data, and thus are most suited for data that cannot be easily displayed in a table [1]. This is often the case when there is a trend or comparison to be shown [2]. Some displays, such as histograms, are in essence graphical [1].

Graphical displays should:

  • show the data
  • induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else
  • avoid distorting what the data is telling
  • present many numbers in a small place
  • make large data sets coherent
  • encourage the eye to compare different pieces of data
  • reveal the data at several levels of detail, from a broad overview to the fine structure
  • serve a reasonably clear purpose: descriptions, exploration, tabulation, or decoration
  • be closely integrated with the statistical and verbal descriptions of the data set [3]

In this part of the chapter, the use of line graphs, histograms, frequency polygons, bar graphs, pie graphs, and other types of data display are discussed.


1. Altman DG. Practical statistics for medical research. London: Chapman & Hall; 1991. p. 43.

2. McLennan W. 1331.0 Statistics - a powerful edge! 2nd ed. Australian Bureau of Statistics; 1998. p. 103.

3. Tufte ER. The visual display of quantitative information. 2nd ed. Connecticut: Graphics Press; 2009. p. 13