SIMPLE RANDOM SAMPL
ING_stud
.doc
Jan 25’10
THE SIMPLE RANDOM SAMPLE
DEFINITION.
Statistical inference
is a procedure by which we reach a conclusion
about a population on the basis of the information contained in a
sample
that has
been drawn from that population
. (RECALL: sample is a
subset
, or
part
of a
population.)
There are many kinds of samples that may be drawn from a population, but only
scientific samples
can be used for making
valid
inferences about a population. The
simplest of them is the
simple random sample
.
DEFINITION.
If a sample of size
n
is drawn from a population of size
N
(N > n) in
such a way that every element in the population has an
equal chance
of being chosen as a
part of the sample,
then such a sample is called
a simple random sample
.
Correspondingly,
every
possible sample of size
n
has the
same chance
of being selected.
(A size of the population (or a sample) is the number of entities in it).
{In general, a procedure used to collect the sample data is called
sampling plan
or
sample design
[2].}
The mechanics of drawing a sample
in accordance with the above
definition
is called
simple random sampling
.
The sampling may be performed
with
replacement
, or
without
replacement
.
The sampling WITH replacement
assumes that
every
member of a population is
available at
each
draw. For example, let us consider the sampling which involves
selecting (from the shelves in a hospital’s medical records department) a sample of
[enumerated – M.V.] charts for collecting information of some kind about patients. The
sampling with replacement assumes that we select a chart to be in the sample, record the
data of interest, and
return
the chart
to
the shelf
. In such a case, the “population” of charts
remains the same, and every chart may be drawn again on some subsequent draw, and the
same data about the same patient will again be recorded.
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 Spring '05
 Kzaer
 Simple random sample, random starting point

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