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
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.
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
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
o The population may change (i.e. births/maturity/deaths and changes
A sample done in a shorter time frame can virtually eliminate
o Double counting (i.e. someone has a primary and a secondary
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
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
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.
o Assigning sequential numbers to each individual in the population
and then randomly selecting numbers and finding the person they
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