To find things out about a population of interest,
it is common practice to take a sample. A sample is a selection of objects and observations
taken from the population of interest. For example,
a population might be all apples in an orchard at a given time.
We wish to know how big the apples are. We can't measure all of them,
so we take a sample of some of them and measure them.
The method chosen for taking the sample depends on the nature of the population,
and the resources available in terms of time and money.
The ideal is for each object in a population to be equally likely to be
chosen as part of the sample.
This is called an unbiased sample. It is also desirable for the sample to be
representative of the population.
if the population of apples were two thirds red and one-third green,
the sample should be similarly split. Note that no matter what we do, there will
always be sampling error
or variation due to sampling, as we are looking at a part of the population,
not the whole population. The video on variation covers these concepts more thoroughly.
This video presents five methods of sampling:
For each method we will outline the process
and the advantages and disadvantages.
Simple random sampling is theoretically the ideal method of Sampling.
You list each member the population and use random numbers to decide which
objects are in the sample. Each object is equally likely to be selected.
This produces an unbiased sample which we hope is representative.
However it can be difficult and expensive
to take a simple random sample when dealing with people. Simple random
sampling is more practical when the population is geographically concentrated
and when a good sampling frame exists. A sampling frame is a list of all the people or objects
in the population interest. Simple random sampling can be more easily implemented
for natural and manufacturing populations.
Convenience sampling is just that: Convenient!
You ask people nearby, or people who walk past at a shopping mall,
or you take the next 20 objects off the production line.
You do what is easy or convenient. Convenience samples are often biased in some way.
But, for a quick and cheap poll it may not really matter.
Convenience samples can also have self-selection bias when people choose
to participate, because they have an interest in the issue in question.
With systematic sampling you choose a starting point at random,
and then systematically take objects at a certain number apart.
For example, if there are a thousand in the population
and you want a sample of fifty, you would take every twentieth object.
Systematic samples are easier to administer than simple random samples
and are usually a good approximation of a random sample.
However, if there's a pattern in the population,
certain types of objects could be chosen more or less often than others.
Cluster sampling the population is divided into clusters
which are then chosen at random.
For example, departments of a business can be clusters,
or suburbs within a city. Within each cluster,
all of the objects are included in the sample. Cluster sampling can be more
convenient and practical
than simple random sampling. However, if the clusters are different from each other
with regard to the elements we are measuring,
it can lead to bias or non-representativeness.
Stratified sampling seems like cluster sampling,
but the strata, or groups, are chosen specifically to represent different
characteristics within the population,
such as ethnicity, location, age
or occupation. Within each group,
a random sample is taken, sometimes in proportion to the size of the group.
Stratified sampling can lead to a very good random representative sample.
However it can be complex to administer, and a sampling frame with considerable
information about the population is required.
There are other sampling methods. The five explained here give an idea of the advantages
and disadvantages of various methods.
You should attempt to use a sampling method that produces the best result
for the resources you have available.
If your sample has known bias, this should be taken into account
in analysis and reporting.