The preceding section has covered the most common problems associated with statistical studies. The desirability of a
sampling procedure depends on both its vulnerability to error and its cost. However, economy and reliability are
competing ends, because, to reduce error often requires an increased expenditure of resources. Of the two types of
statistical errors, only sampling error can be controlled by exercising care in determining the method for choosing the
sample. The previous section has shown that sampling error may be due to either bias or chance. The chance component
(sometimes called random error) exists no matter how carefully the selection procedures are implemented, and the only
way to minimize chance sampling errors is to select a sufficiently large sample (sample size is discussed towards the
end of this tutorial). Sampling bias on the other hand may be minimized by the wise choice of a sampling procedure.
TYPES OF SAMPLES
There are three primary kinds of samples: the convenience, the judgement sample, and the random sample. They differ in
the manner in which the elementary units are chosen.
The convenient sample
A convenience sample results when the more convenient elementary units are chosen from a population for observation.
The judgement sample
A judgement sample is obtained according to the discretion of someone who is familiar with the relevant characteristics
of the population.
The random sample
This may be the most important type of sample. A random sample allows a known probability that each elementary unit
will be chosen. For this reason, it is sometimes referred to as a probability sample. This is the type of sampling that is
used in lotteries and raffles. For example, if you want to select 10 players randomly from a population of 100, you can
write their names, fold them up, mix them thoroughly then pick ten. In this case, every name had any equal chance of
being picked. Random numbers can also be used (see Lapin page 81).