Interpreting data: boxplots and tables
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# 2.2 Basic table layout

As Table 2.1 stands, it is hard to assimilate the information. Indeed it is not at all clear what any of the numbers mean. Even doing something as simple as giving the columns proper headings and drawing a few lines to separate the headings from the rest of the data, as in Table 2.2, make a big difference to clarity (guideline 1).

## Table 2.2 South Australia: incidence and mortality for lung cancer, 1981

Age group | Population size | New cases | Deaths
--- | --- | --- | ---
| Male | Female | Male | Female | Male | Female
0–4 | 47589 | 45273 | 0 | 0 | 0 | 0
5–9 | 53814 | 50672 | 0 | 0 | 0 | 0
10–14 | 58561 | 55645 | 0 | 0 | 0 | 0
15–19 | 59408 | 57756 | 0 | 0 | 0 | 0
20–24 | 58443 | 57249 | 0 | 0 | 0 | 0
25–29 | 54341 | 53376 | 0 | 0 | 1 | 0
30–34 | 53456 | 52978 | 1 | 0 | 1 | 0
35–39 | 42113 | 41988 | 0 | 2 | 0 | 0
40–44 | 35648 | 35547 | 2 | 5 | 3 | 3
45–49 | 32911 | 31799 | 8 | 2 | 10 | 2
50–54 | 36485 | 35333 | 38 | 8 | 26 | 8
55–59 | 35192 | 35555 | 61 | 18 | 43 | 8
60–64 | 28131 | 30868 | 67 | 16 | 57 | 15
65–69 | 24419 | 27390 | 88 | 15 | 69 | 17
70–74 | 16613 | 21402 | 60 | 21 | 61 | 21
75–79 | 9958 | 14546 | 46 | 10 | 46 | 9
80–84 | 4852 | 9749 | 24 | 6 | 23 | 4
85+ | 2790 | 7477 | 7 | 2 | 8 | 3

There is, of course, still an enormous amount of information to absorb, but the labelling is better and, above all, the table is more or less self-explanatory.

But it is important to consider what information we really want the table to convey to the reader. Here there are often choices to be made. Table 2.2 includes data on the population size in different age groups, and these data could be used to investigate the average age of the population, or the way in which the proportions of people in different age groups differ between males and females. If we wanted to convey this particular kind of information, it would make sense to simplify the table in various ways — for instance, all the data about lung cancer cases and deaths could simply be omitted! But, for this particular data set, it is much more likely that we would be interested primarily in the lung cancer cases and deaths, and in that case we would be interested in the population counts only insofar as they are related to the lung cancer counts. In that case, there is an immediate and obvious simplification to be made. There were no lung cancer cases or deaths in people aged up to 24, so we can simply pool together the first five rows of the table as in Table 2.3.

## Table 2.3 South Australia: incidence and mortality for lung cancer, 1981

Age group | Population size | New cases | Deaths
--- | --- | --- | ---
| Male | Female | Male | Female | Male | Female
0–24 | 277815 | 266595 | 0 | 0 | 0 | 0
25–29 | 54341 | 53376 | 0 | 0 | 1 | 0
30–34 | 53456 | 52978 | 1 | 0 | 1 | 0
35–39 | 42113 | 41988 | 0 | 2 | 0 | 0
40–44 | 35648 | 35547 | 2 | 5 | 3 | 3
45–49 | 32911 | 31799 | 8 | 2 | 10 | 2
50–54 | 36485 | 35333 | 38 | 8 | 26 | 8
55–59 | 35192 | 35555 | 61 | 18 | 43 | 8
60–64 | 28131 | 30868 | 67 | 16 | 57 | 15
65–69 | 24419 | 27390 | 88 | 15 | 69 | 17
70–74 | 16613 | 21402 | 60 | 21 | 61 | 21
75–79 | 9958 | 14546 | 46 | 10 | 46 | 9
80–84 | 4852 | 9749 | 24 | 6 | 23 | 4
85+ | 2790 | 7477 | 7 | 2 | 8 | 3

This simplification, in line with guideline 2, has not lost any information about lung cancer at all, and the table is now easier to comprehend.

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OpenLearn - Interpreting data: boxplots and tables
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