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Chapter 3

SPAN 101 Chapter Notes - Chapter 3: Sampling Frame, Stratified Sampling, Simple Random Sample

Course Code
SPAN 101
Enrique Manchon

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Sample survey: designed to ask questions of a small group of people in the hope of
learning something about the entire population
Idea 1: Examine a part of the whole
a sample selected form the population
Idea 1: Randomize
on average the sample looks like the rest of the population
nobody can guess the outcome before it happens
underlying set of outcomes will be equally likely
variability from sample to sample is called sampling error
Idea 3: The sample size is what matters
the size of the sample determines the data regardless of the size of the
what fraction of the population you sample doesn’t matter
A Census:
difficult to complete a census
Hard to Locate or hard to measure
Population we’re studying may change
Census can be a cumbersome multiple addresses or none
Parameters: key numbers in models of reality
Statistic: any summary found form the data estimate
Population: all Canadian households
Parameter: mean household income of population
Sample: 1000 households contacted by researchers
Statistic/ Estimate: mean household income of the 1000 households
Simple Random Sample: combination of individuals has an equal chance of being
Stratified Sampling: slice the population into homogeneous groups and then use
simple random sampling within each stratum, coming the results. Reduces sample
to sample variability. Are homogeneous but differ form one another
Cluster and Multistage Sampling: splitting the population into parts or clusters that
each represent the population can make sampling more practical. Heterogeneous
and resembling the overall population
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