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Lecture 14

STAT 101 Lecture Notes - Lecture 14: Sampling Frame, Simple Random Sample, Standard DeviationPremium


Department
Statistics
Course Code
STAT 101
Professor
Richard Waterman
Lecture
14

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Stat 101 - Introduction to Business Statistics - Lecture 14: Sampling
Sample Paradigm
You can’t survey an entire population, and so you can survey a sample represenatative
of the population to draw conclusions about the population
Use “N” for the population size and “n” for the sample size
Representativeness can be broken by:
Respondant discretion - Non-response
Interviewer discretion
Key question: is the reason for non-response related to the attribute you are
trying to measure? (ie. Illegal aliens)
Good samples are probability samples for which each unit in the population has a known
probability of being in the sample
Simple Random Sample: Simplest case; equal probability sample, each unit has
the same chance of being in the sample
Whenever data goes through a filter sampling bias is potentially introduced
● Ideally:
You have a complete accurate list of ALL units in the target population, called
sampling frame
From here you draw a SRS
In reality there are practical constraints on the simple random sample (cost and
time of sampling)
Error Types
Sampling Error
Due to the fact that we did not see the entire population
Different samples would give slightly different estimates (sampling variability)
The larger the sample, the lower the sampling error
Non-sampling Error
these do not get smaller as sample size increases
Sample Size
So long as the sample size is small with respect to the population size (10% or less),
then the sample size required to reach a certain level of precision is independent of the
population size
Ex) : I am going to do an opinion poll in California and Wyoming. Do I need to take a
bigger sample in California as it has more people?
No - take the same size sample in each state
Other Sampling Processes (besides SRS)
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