Sampling techniques
We want to make some inferences about some population, based on data available from a sample.
Sampling errors refer to uncertainties that arise due to extrapolation, and generally cannot be avoided. Bias refers to flaws in the data collection or analysis that can be avoided. Typically, bias means the population that the sample is representative of, is different from the target population that we want to learn more about.
Sampling techniques
| technique | description | advantages | disadvantages |
|---|---|---|---|
| simple random | treat population as one group | easy | unreliable if the population is varied in composition |
| convenience | take nearest or a cluster of values | useful if population is uniform | generally unreliable |
| systematic | take every value | sample over entire population | less able to take fewer or more samples, susceptible to missing or overemphasizing patterns, tedious |
| quota | convenience sampling for groups | somewhat representative of the population if groups are well-chosen | sometimes unreliable |
| stratified | simple random sampling for groups | representative of the population if groups are well-chosen | more technical in the set up |
IA moderation mostly uses stratified sample, where works are chosen to cover the whole spread of marks.