Intended for healthcare professionals

Student Education

# Summarising information

BMJ 2002; 325 (Published 01 September 2002) Cite this as: BMJ 2002;325:0209311
1. Wai-Ching Leung, locum general practitioner1
1. 1Norwich

In the third article in our series on medical statistics, Wai-Ching Leung discusses the best way to condense different types of data

In my previous articles I noted that we can use statistics when events are not entirely predictable and depend on a large number of factors.12 Statistics can also be used to test hypotheses and to estimate how likely it is that something will happen from information gathered from a large number of observations.

## Why summarise?

Effective techniques to summarise a large amount of information are essential for several reasons. Firstly, this will help us to make sense and get a feel of the information we have. Graphs and charts often prove useful. Secondly, it may help us to formulate the hypothesis we wish to test. For example, if we have the heights of a large number of men and women and we want to show that, overall, men are taller than women, we must have a method of calculating “average” height (averaging is an example of summarising information from a large number of observations). It would be ideal if we could summarise the information both accurately and simply. In practice, however, a compromise is often necessary. Information is often summarised at the expense of accuracy.

It is important to note that there are three types of data, which differ in the kind of information they provide and in the ways they can be summarised and analysed.

## Types of data

The three different types of data are categorical, ranked, and interval data. You may have seen them called by different names in older textbooks so these names are given in brackets.

## Categorical (nominal) data

Observations are grouped by name into categories, but they are not graded in any way. In other words, it is not possible to say one category is higher or lower than …

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