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Chapter 3

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University of Toronto St. George

Economics

ECO220Y1

Jennifer Murdock

Fall

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ECO220Y1
Textbook Notes
Chapter 3: Surveys and Sampling
3.1 Three Features of Sampling
1. Examine a Part of the Whole
o Population: the entire group of individuals or instances about whom
we hope to learn.
o Sample: a subset of a population, examined in the hope of learning
about the population.
o Sample survey: a study that asks questions of a sample drawn from
some population in hopes of learning something about the entire
population.
o Biased: said to occur when the summary characteristics of a sample
differ from the corresponding characteristics of the population it is
trying to represent.
o In order to make a sample as representative as possible, individuals
should be selected at random.
2. Randomize
o Randomizing protects us by giving us a representative sample even
for effects we were unaware of (i.e. peas in soup).
It makes sure that, on average, the sample looks like the rest of
the population.
o Randomization: a defence against bias in the sample selection process,
in which each individual is given a fair, random chance of selection.
o Pseudorandom: numbers generated by a computer program; they are
not technically random.
o Sampling variability: the differences that exist from sample to sample.
3. The Sample Size is What Matters
o The size of the sample determines what we can conclude from the
data regardless of the size of the population.
I.e. too small a sample will yield less accurate data.
3.2 A Census – Does It Make sense?
Census: an attempt to collect data on the entire population of interest
(sample becomes the population).
Problems with attempting a census:
o May be difficult to complete (i.e. hard to contact every single person in
the population).
o The population may change (i.e. births/maturity/deaths and changes
in tastes/preferences).
A sample done in a shorter time frame can virtually eliminate
this issue.
o Double counting (i.e. someone has a primary and a secondary
residence).
3.3 Populations and Parameters Models of data can give us summaries that we can learn from and use even
though they don’t fit each data value exactly.
Parameter: a numerically valued attribute of a model for a population.
o The value is rarely known; rather the hope is to estimate it from
sampled data.
Population parameter: a parameter used in a model for a population.
Statistic: a value calculated from sampled data, particularly one that
corresponds to, and thus estimates, a population parameter.
o Sample statistic: usually used to parallel the term “population
parameter.”
Representative sample: a sample from which the statistics computed
accurately reflect the corresponding population parameters.
3.4 Simple Random Sampling (SRS)
Simple random sampling (SRS): a sample in which each set of n individuals in
the population has an equal chance of selection.
Sampling frame: a list of individuals from which the sample is drawn.
Individuals in the population of interest but who are not in the sampling
frame cannot be included in any sample.
Possible methods:
o Assigning sequential numbers to each individual in the population
and then randomly selecting numbers and finding the person they
correspond to.
o Generating random numbers for each individual in the population and
then sorting numerically and then picking a random sample of any
size off the top of the sorted list.
3.5 Other Random Sample Designs
Strata: subsets of a population that are internally homogeneous but may
differ from one another.
Stratified random sample: a sampling design in which the population is
divided into several homogeneous subpopulations, or strata, and random
samples are then drawn from each stratum.
o Allows for information to be obtained about each stratum as well as
the population as a whole.
o When samples are unable to be obtained in

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