If the results of a study reveal an interesting association between some exposure and a health outcome, there is a natural tendency to assume that it is real. (Note that we are considering whether two things are associated. This does not necessarily imply a causal association. We will discuss approaches to determining causality further in Chapter 10.) However, before we can even contemplate this possibility we have to attempt to rule out other possible explanations for the results. There are three main ‘alternative explanations’ that we have to consider whenever we analyse epidemiological data or read the reports of others: namely, could the results be due to

• chance

• bias or error or

• confounding?

We will discuss the first of these, chance, in this chapter and will cover bias and confounding in Chapters 7 and 8.

Random sampling error

When we conduct a study or survey it is rarely possible to include the whole of a population so we usually have to rely on a sample of that population and trust that this sample will give us an answer that holds true for the general population. If we select the sample of people wisely and they are truly representative of the target population (the population that we want to study) then we will not introduce any selection bias into the study (see Chapter 7).