Introduction

One way of generating hypotheses is to collect data and look for patterns. Often, however, it is difficult to see any pattern from a set of data, which may just be a list of numbers. Graphs and descriptive statistics are very useful for summarising and displaying data in ways that may reveal patterns. This chapter describes the different types of data you are likely to encounter and discusses ways of displaying them.

Variables, experimental units, and types of data

The particular attributes you measure when you collect data are called variables (e.g. body temperature, the numbers of a particular species of beetle per broad bean pod, the amount of fungal damage per leaf, or the numbers of brown and albino mice). These data are collected from each experimental unit, which may be an individual (e.g. a human being or a whale) or a defined item (e.g. a square metre of the seabed, a leaf, or a lake). If you only measure one variable per experimental unit, the data set is univariate. Data for two variables per unit are bivariate, while data for three or more variables measured on the same experimental unit are multivariate.

Variables can be measured on four scales – ratio, interval, ordinal, or nominal.

A ratio scale describes a variable whose numerical values truly indicate the quantity being measured.