The Summary from class is outlined below. It outlines
our textbook, " Lind. (2005). Statistical techniques in business & economics
(11th ed). New York: McGraw-Hill, Chapter 8."
What is a Sample?
"A probability sample is a sample selected such that each item
or person in the population being studied has a known likelihood of being
included in the sample."
Types of Sampling
Simple Random Sample: A sample formulated so that
each item or person in the population has the same chance of being included.
Systematic Random Sampling: The items or individuals
of the population are arranged in some order. A random starting point is
selected and then every kth member of the population is selected for the
sample. 1-in-L samples. A sample taken by moving
systematically through the population. One might randomly select one the
first 200 population units and then systematically sample every 200th
population unit thereafter.
Stratified Random Sampling: A population is first
divided into subgroups, called strata, and a sample is selected from each
stratum. Simple random
samples from non-overlapping subpopulations or strata. Could be (1)
Partitioned (Divided) Population, (2) Non-overlapping subpopulations, or (3)
(Strata) Difference between groups to compare differences
Cluster Sampling: A population is first divided into
primary units then samples are selected from the primary units.
Random samples of “clusters” of units. Could be (1) Random samples of
“clusters” of units or (2) Very large populations
In nonprobability samples, inclusion in the sample is
based on the judgment of the person selecting the sample.
Why Sample? Sample if:
It is physical impossible to check all items in the
The cost of studying all the items in a population is too
The sample results are usually adequate.
Contacting the whole population is too time-consuming.
The destructive nature of certain tests profits testing all
element in the population.
The sampling error is the difference between a sample
statistic and its corresponding population parameter.
The sampling distribution of the sample mean is a
probability distribution consisting of all possible sample means of a given
sample size selected from a population.
Central Limit Theorem:
For a population with a mean
: and a variance
the sampling distribution of the means of all possible samples of size n
generated from the population will be approximately normally distributed.
The mean of the sampling distribution equal to
: and the variance equal to
Are one value ( a single point) that is used to estimate a
population parameter. Such as:
the sample standard deviation,
the sample variance,
the sample proportion.
If a population follows the normal distribution, the sampling
distribution of the sample mean will also follow the normal distribution. To
determine the probability a sample mean falls within a particular region, use:
If the population does not follow the normal distribution, but
the sample is of at least 30 observations, the sample means will follow the
normal distribution. To determine the probability a sample mean falls within a
particular region, use: