Random sampling or probability sampling is a sampling technique in which each sample from a larger population has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population.
It is usually more accurate, but more expensive than non-probability sampling.
There are essentially 4 types of non-random sampling methods:
1. Simple – each member of the population has an equal chance of being selected. A random generator is used and the member is picked.
2. Systematic – each member of the population is listed with a number, but instead of using a randomly generated number, individuals are picked at a chosen regular interval
3. Stratified – we divide the population into subpopulations (strata), based on relevant characteristics (age, gender color). This method works best if each stratum is homogeneous.
4. Cluster – here you also subdivide the population into subgroups, but each subgroup has similar characteristics to the whole sample.
Imagine the same type of office replicated in different countries. Here you choose one office as a representation of the group. You have been to a Starbucks, so essentially are doing your assumptions on one and extrapolating these to the other ones: one manager, 4 baristas and so on.
