Simple Random Sample
Of N measurements from a population is a subset of the population selected in a manner such that every sample of size N form the population has an equal chance of being selected.
A numerical facsimile or representation of a real world phenomenon.
Use a simple random sample from the entire population.
Divide the entire population into distinct subgroups called strata. The strata are based on specific characteristics such as age, income education level, and so on. All member of the stratum share the specific characteristic. Draw random from each stratum.
Number all members of the population sequentially. Then, from a starting point selected at random, include every Kth member of the population in the sample.
Divide the entire population into pre-existing segments or clusters. The clusters are often geographic. Make a random selection of the clusters. Include every member of each selected cluster in the sample.
Use a variety of sampling methods to create successfully smaller groups at each stage. The final sample consists of clusters.
Create a sample by using a data from population members that are readily available.
A list of individuals from which a sample is actually selected.
Results from omitting population members from the sample frame.
The difference between measurements from a sample and corresponding measurements from the respective population. It is caused by the fact that the sample does not perfectly represent the population.
The result of poor sampling design, sloppy data collection, faulty measuring instruments, bias in questionnaires and so on.