ISDS ch 7
Probability Sample
sample in which items are selected based upon known probabilities.
Central limit theorem
states that the sum or mean of a large number of independent observations from the same underlying distribution has an approximate normal distribution. the approximation steadily improves as the number of observations increases.
sample
subset of the population
Sampling Error
the error incurred by taking a sample instead of a census.
sampling distribution
the probability distribution of an estimator
Bias
the tendency of a sample statistic to systematically over- or under estimate a population parameter
sample statistic
used to make inferences about an unknown population parameter
Measurement Error
when data collected do not reflect the true measures.
parameter
a constant characteristic of a population
cluster sampling
a population is first divided up into mutually exclusive and collectively exhaustive groups, called clusters. A cluster sample includes observations from randomly selected clusters.
stratified random sampling
a population is first divided up into mutually exclusive and collectively exhaustive groups, called strata. A stratified sample includes randomly selected observations from each stratum, which are proportional to the stratum's size
Non-probability Sample
a sample in which the items have unknown probabilities of being selected.
simple random sample
a sample of n observations which has the same probability of being selected from the population as any other sample of n observations.
nonresponse bias
a systematic difference in preferences between respondents and non respondents to a survey or poll
selection bias
a systematic exclusion of certain groups from consideration of the sample
population
all items in a statistical problem