statistics dc - 1.2 terms (random samples)

simple random sample (SRS)

a subset of the population selected in such a manner that every sample of size n from the population has an equal chance of being selected.

features of an SRS

* every sample of the specified size/population has an equal chance of being selected
* no research bias occurs in the sample
* a random sample may not always reflect the diversity of the population *(ex. population is 10 cats+10 dogs, SRS gives 6 cats)*

random number table

a solid mass of digits that has been broken up into rows and blocks for user convenience.

stratified sampling

divide the population into distinct subgroups called “strata” based on specific characteristics (*ex. age, income, education level, etc).* draw random samples from each group

systematic sampling

number all the members of the population sequentially, then from a random starting point, include every nth member of the population in the sample.

cluster sampling

dividing the population into pre-existing segments or clusters, often geographic. make a random selection of clusters amd do a census on each select cluster.

multistage sample

use a variety of sampling methods to create successively smaller groups at each stage

convenience sampling

create a sample by using data from population members that are readily available

sample frame

the list of individuals from which a sample is actually selected

undercoverage

results from omitting population members from the sampling frame

sampling error

the difference between measurements from a sample and corresponding measurements from the respective population. caused by the fact that the sample does not perfectly represent the population.

non-sampling error

the result of poor sample design, sloppy design, sloppy data collection, faulty measuring instruments, bias in questionnaires, and so on.