## 5.2 Precision and accuracy

Two issues concerning sampling,
raised in Sect. 5.1,
were:
*which* individuals should be in the sample,
and *how many* individuals should be in the sample be.
These two issues address two different aspects of sampling:
**precision** and **accuracy**
(Fig. 5.1).

**Accuracy** refers to how close
a *sample* estimate is to the *population* value (on *average*).
**Precision** refers to how close all the possible sample estimates are likely to be
(that is, how much variation is likely in the sample estimates).

**Definition 5.1 (Accuracy)**

*Accuracy*refers to how close a

*sample*estimate is to the

*population*value, on average.

**Definition 5.2 (Precision)**

*Precision*refers to how close the sample estimates from different samples are likely to be to each other.

Using this language:

- The
*type*of sampling (i.e., the way in which the samples in selected) impacts the*accuracy*of the sample estimate. In other words, the type of sampling impacts the*external validity*of the study. - The
*size*of the sample impacts the*precision*of the sample estimate.

For example,
large samples are more likely to be *precise* estimates
because each possible sample value will produced similar estimates,
but they may or may not be accurate estimates.
Similarly,
random samples are likely to produce *accurate* estimates
(and hence the study is more likely to be externally valid),
but they may not be *precise* unless the sample is also large.

**Example 5.2 (Precision and accuracy) **To estimate the average age of *all Queenslanders*,
we could ask 9000 Queensland school children (a large sample indeed!).

*precise*answer because the sample is large, but

*inaccurate*answer because the sample is not representative of

*all*Queenslanders. In fact, the sample may give a precise answer to a

*different*question: ‘What is the average age of Queensland school children?’