The purpose of sampling is to determine the
characteristics of a population using a relatively
small sample taken from the population.
If a sample is to give meaningful results it
must be representative of the population. There
are various ways of attempting to achieve this.
Convenience Sampling is a sampling
method in which units are selected based on easy
access/availability. The disadvantage of convenience
sampling is that the units that are easiest to
obtain may not be representative of the population:
- products on top of a box of parts may be
a different quality from those at the bottom
- people who are at home when the market researcher
calls may not be representative of the entire
Random Sampling involves taking
items for the sample in a way that ensures that
any unit in the population has an equal chance
of being selected. This is intended to ensure
that the sample is not biased by the method of
collection. Bias may be inadvertently introduced
by selecting items that are easy to collect, or
Stratified Sampling is a sampling
method in which the population is split into several
categories that share common characteristics.
Items are collected at random from each category,
in proportion to the size of the category relative
to the population. Stratified sampling may give
more reliable results than pure random
sampling because it ensures that all
categories are fairly represented.
Systematic Sampling involves
taking a sample from the available data in a set
pattern, rather than at random. A telephone survey
that called every hundredth caller listed in the
phone book would be a systematic sample. Systematic
sampling is often convenient and economical, but
carries the risk that there will be an unsuspected
systematic pattern in the data.
For more information on sampling refer to
Quality Six Sigma Glossary.