Estimation

The aim of sampling is usually to estimate one or more population values (parameters) from a sample. A following chapter deals in depth with this issue of estimation, but we mention here that estimates such as sample means or proportions are random quantities. If we were to repeat the sampling process, the estimate would vary and this sample-to-sample variability can be described by a distribution (e.g. the distribution of the sample mean or sample proportion).

The estimate is not guaranteed to be the same as the value that we are estimating, so we call the difference the error in the estimate. There are different kinds of error.

Sampling error

We have presented four different ways to sample from a population

Each of these involves randomness in the sample-selection process, so the estimated mean or proportion is unlikely to be exactly the same as the underlying population parameter that is being estimated. This kind of error is called sampling error.

When sampling books from a library or sacks of rice from the output of a factory, sampling error is the main or only type of error.

Non-sampling error

When sampling from some types of population — especially human populations — problems often arise when conducting one of the above sampling schemes. For example, some sampled people are likely to refuse to participate in your study.

Such difficulties also result in errors and these are called non-sampling errors. Non-sampling errors can be much higher than sampling errors and are much more serious.

It is therefore important to design a survey to minimise the risk of non-sampling errors. The following pages discuss various types of non-sampling error.