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