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Interpreting data: Boxplots and tables
Interpreting data: Boxplots and tables

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2.1 Data sets in different tabular forms

Example 2.1 Lung cancer deaths in South Australia

Table 2.1 contains raw data on the incidence and mortality for lung cancer in South Australia in 1981.

Table 2.1 Age group, male and of population sizes, male and female cases, male and female deaths
0–447589452730000
5–953814506720000
10–1458561556450000
15–1959408577560000
20–2458443572490000
25–2954341533760010
30–3453456529781010
35–3942113419880200
40–4435648355472533
45–49329113179982102
50–543648535333388268
55–5935192355556118438
60–64281313086867165715
65–69244192739088156917
70–74166132140260216121
75–799958145464610469
80–8448529749246234
85+279074777283
O'Neill, T. J., Tallis, G. M. and Leppard, P. (1985) The epidemiology of a disease using hazard functions. Australian Journal of Satistics, 27, 283–297.

A table like Table 2.1 may be adequate for someone who is merely taking a quick look at the data, perhaps prior to carrying out an analysis, but it is not the best way of presenting the figures to most readers. The objectives in producing a table that is actually being used to communicate information are to make the data immediately clear, and to facilitate picking out important patterns in them with the minimum of effort. To this end, there are several guidelines for producing tables which should be borne in mind.

Guidelines for tables

  1. Labelling of rows and columns should be clear and unambiguous.

  2. A table should contain the minimum amount of information needed to communicate its message. This may involve splitting the data into several simpler tables or pooling cells.

  3. It may be appropriate to simplify the numbers in a table to aid speedy comprehension.

  4. Useful summary statistics or calculation results should be added, where appropriate, to help communicate the message.

These guidelines will be followed in relation to Table 2.1 to see what changes they suggest.