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Q
uantitative
R
easoning &
P
roblem
S
olving
148
© 2014 Pacific Crest
4. Based on your own experiences and the answers you have given to questions 1 through 3, why could
sampling be biased?
5. Howmight a small sample from a large population actually provide accurate and valuable information
about the population?
M
athematical Language
Terms and notation
cluster sampling
— it partitions a population into sets and then selects a sample of participants
proportionately to the size of each set (e.g., sets could be organizations, locations, or demographics)
●
one-stage
uses either simple random, systematic, or stratified random sampling to select the
participants
●
two-stage
just takes this process a step further, and again selects from within the first group to
obtain the final group to study
convenience sampling
— sampling that is conducted easily on available data
nonprobability sampling
— sampling done without randomness
probability sampling
— the sampling techniques that use randomness
purposeful sampling
— a sample is drawn to meet a specific need or purpose
quota sampling
— sampling is done until a specific number of observations for various sub-
populations have been selected
random sample
— a sample where every participant in the population has the same probability of
being selected
sample
size
— the number of data points in a sample
sampling bias
— in survey sampling,
bias
refers to a sample statistic systematically over- or under-
estimating a population parameter causing sampling error that cannot be fixed by increasing the
sample size
sampling distribution
— the probability distribution formed by completing repeated samples of a
population examining a particular statistic
sampling error
— the difference between the population parameter and the sample statistic is the
sampling error
simple random sampling
— collect a sample where each item has an equal chance of being selected
standard error
— the standard deviation of the sampling distribution of a sample statistic
stratified sampling
— a sampling strategy that involves separating a population into mutually
exclusive sets and then drawing a random sample from each set
systematic random sampling
— creating a sample from the population by selecting every k
th
observation
target population
— the population that you are trying to analyze
voluntary sampling
— participants volunteer to be part of a sample