Discrete and continuous data

In this section, we distinguish between two types of numerical data.

Discrete data
When the values in the batch must be whole numbers (counts), the data set is called discrete. Examples of discrete measurements are:
Continuous data
When the data are not constrained to be whole numbers, the data set is called continuous. Examples are:

Dot plots for counts

Dot plots can be used to display count data. However when the counts are all small, many of the values usually appear several times. Basic dot plots are therefore misleading since repeated values are superimposed and appear as a single cross.

However stacked and jittered dot plots can still be used. If the counts are all small, no information need be lost by stacking since there can be a column of crosses for each distinct value.

Morally 'right' actions

The following table gives scores from 106 volunteers on a 'motivation scale'. The subjects were presented with 37 situations and could choose one of two possible actions in response to each situation. One of these satisfied short-term gains and the other was a more morally "right" action. The score for each subject was the number of morally right actions chosen (a count between 0 and 37).

13
17
13
8
7
10
10
21
18
17
19
15
15
23
12
12
15
27
19
23
6
2
9
11
5
18
20
24
15
14
11
2
4
4
13
10
13
19
25
14
11
4
14
12
23
19
17
16
17
13
7
9
12
11
30
19
4
11
18
18
24
15
13
12
6
17
27
3
10
7
1
14
22
16
10
2
7
9
5
21
18
17
18
12
15
13
13
15
6
25
13
15
5
28
20
19
14
11
14
4
8
10
7
23
18
24

The diagram below shows an unjittered dot plot of the data.

Observe that the basic dot plot gives no indication of the distribution of choices — there is a cross for most possible counts, even though some of these crosses represent several volunteers.

Use the pop-up menu to display jittered and stacked dot plots of the data. The stacked dot plot is the best display of these data.