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Lecture 14

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

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STAT 101Professor

Richard WatermanLecture

14This

**preview**shows half of the first page. to view the full**3 pages of the document.**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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