Simple Random Sample (SRS)
There is a variety of ways in which one could choose a sample from a population. A simple random sample (SRS) is one of the most typical ways. Also commonly referred to as a probability sample, a simple random sample of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance of being in the selected sample. An example of an SRS would be drawing names from a hat. An online poll in which a person is asked to given their opinion about something is not random because only those people with strong opinions, either positive or negative, are likely to respond. This type of poll doesn't reflect the opinions of the apathetic .
Online Opinion Polls
Online and phone-in polls also produce biased samples because the respondents are self-selected. In self-selection bias, those individuals who are highly motivated to respond-- typically individuals who have strong opinions-- are over-represented, and individuals who are indifferent or apathetic are less likely to respond.
Simple random samples are not perfect and should not always be used. They can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population. For instance, a simple random sample of ten people from a given country will on average produce five men and five women, but any given trial is likely to over-represent one sex and under-represent the other. Systematic and stratified techniques, discussed below, attempt to overcome this problem by using information about the population to choose a more representative sample.
In addition, SRS may also be cumbersome and tedious when sampling from an unusually large target population. In some cases, investigators are interested in research questions specific to subgroups of the population. For example, researchers might be interested in examining whether cognitive ability as a predictor of job performance is equally applicable across racial groups. SRS cannot accommodate the needs of researchers in this situation because it does not provide sub-samples of the population. Stratified sampling, which is discussed below, addresses this weakness of SRS.
Stratified Random Sample
When a population embraces a number of distinct categories, it can be beneficial to divide the population in sub-populations called strata. These strata must be in some way important to the response the researcher is studying. At this stage, a simple random sample would be chosen from each stratum and combined to form the full sample.
For example, let's say we want to sample the students of a high school to see what type of music they like to listen to, and we want the sample to be representative of all grade levels. It would make sense to divide the students into their distinct grade levels and then choose an SRS from each grade level. Each sample would be combined to form the full sample.
Cluster Sample
Cluster sampling divides the population into groups, or clusters. Some of these clusters are randomly selected. Then, all the individuals in the chosen cluster are selected to be in the sample. This process is often used because it can be cheaper and more time-efficient.
For example, while surveying households within a city, we might choose to select 100 city blocks and then interview every household within the selected blocks, rather than interview random households spread out over the entire city.
Systematic Sample
Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every