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

This free course is available to start right now. Review the full course description and key learning outcomes and create an account and enrol if you want a free statement of participation.

Free course

Interpreting data: Boxplots and tables

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.

M248_2

Take your learning further

Making the decision to study can be a big step, which is why you'll want a trusted University. The Open University has 50 years’ experience delivering flexible learning and 170,000 students are studying with us right now. Take a look at all Open University courses.

If you are new to university level study, find out more about the types of qualifications we offer, including our entry level Access courses and Certificates.

Not ready for University study then browse over 900 free courses on OpenLearn and sign up to our newsletter to hear about new free courses as they are released.

Every year, thousands of students decide to study with The Open University. With over 120 qualifications, we’ve got the right course for you.

Request an Open University prospectus