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Statistical Sampling
BUSA 2100
Sections 7.0, 7.7, 7.2, 7.3
Populations and Samples
Definition: A population is the set of all
items being considered.
 Definition: A sample is a subset
(portion) of the population.
 Samples should be representative
(microcosms) of the population.
 Samples should be “baby populations.”

Use of Samples

Using the entire population is time-consuming,
expensive, and sometimes impossible.

Types of sample surveys used to collect data
about a topic or situation:

Properly selected samples can provide
accurate information, without using the entire
population.
Types of Sampling

Convenience sampling: Just selecting
items that are readily available.
Types of Sampling, Page 2

Random Sampling: Select a sample
so that each item in the population has
an equal probability of being selected.
Types of Sampling, Page 3


Random samples are very high quality.
Disadvantage: They require a numbered list
of the population.
Types of Sampling, Page 4

Systematic Sampling: Beginning at a
random starting point and selecting
every kth item.

As good (or almost as good) as a
random sample, and easier to do.
Types of Sampling, Page 5
Stratified Sampling: Divide the population into strata (groups) and select
random subsamples from each group.
 Then combine the subsamples to do
estimates for the entire population.
 Examples of strata:

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Uses: Nielsen TV ratings, political polls.
Statistical Inference
There are two branches of statistics.
 Descriptive Statistics (1st half of the
course).
 Inferential Statistics (2nd half of the
course).
 Definition: Inferential Statistics (or
Statistical Inference) is using
information from a sample to draw
conclusions about a population.

Statistical Estimates
Population parameters are numerical
values calculated from the population,
e.g. mu and sigma.
 Sample statistics are numerical values
from a sample, e.g. X-bar and s.
 Sample statistics are used as point
estimates of population parameters.
 Using a sample, rather than the entire
population, creates errors in estimation.

Sampling Error
Def.: Sampling Error is the difference
between a sample mean (or proportion)
& the population mean (or proportion).
 The amount of sampling error can be
minimized in at least 2 ways.
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