A *simple random sample* is similar to
putting the names of all students in a hat, and then drawing 200 tickets
*without replacement* .
A more practical procedure is to create a *frame* , or
a list, of all WMU students
and have a computer randomly select 200 names from the frame. On a
smaller scale, one may use a *table of random numbers*
to select subjects from the frame.
The method of simple random
sampling has ideal statistical properties in the sense that each
element in the frame has an equal chance of being selected.
However many target populations have no convenient frames available.
Examples are the populations of Kalamazoo residents,
customers of Meijer Stores, potential credit card customers,
and US registered voters.
In these cases, more convenient frames that simply approximate the target population
may be used, like phone books and mailing lists.

A *k*-in-1 *systematic sample* selects
every *k*th subject on a list or a sequence. For example, every 10th
customer entering the store may be approached for a survey, or every
25th number in the phone book may be called.

A *stratified random sample* partitions
the population into subsets called *strata* , and selects
a simple random sample from each stratum. In the survey of WMU undergraduates,
we may choose to draw 50 students from each of the freshman,
sophomore, junior, and senior strata, instead of 200 students from the
combined student population.

A *cluster sample*
would randomly select, say, 5 classes of 40 students each.
Each class is called a cluster of students. Cluster sampling works best when
heterogenous clusters are available, i.e. each cluster is approximately
representative of the population. This means that classes need to contain
freshmen and seniors, men and women, etc. Therefore, it seems better
to limit the frame to containing general education classes,
because classes for specific majors tend to be less
heterogenous.

The sampling designs discussed above are examples of
*probability samples* ,
because their use of random selection (*of* clusters, or *within* strata)
gives them desirable statistical properties.
Informal and less scientific surveys may conduct
*nonprobability samples* , like randomly approaching
people at the mall or WMU students at the Berhard Center.
Nonprobability samples tend to be less representative of the target population
because interviewers tend to choose more friendly-looking people, or
more attractive people. Furthermore, the shopping mall sample would tend to
exclude segments of the population who don't shop often. Approaching
students at the Bernhard Center may tend to exclude dormitory residents
who have meal plans.

When populations are small, data may be collected on the whole population
instead of a sample. This is called a *census*
survey instead of a sample survey.