In many experiments, sampling an entire population as part of a research experiment is impossible, due to the time, expense and sheer number of subjects.
What is Sampling? Imagine, for example, an experiment to test the effects of a new education technique on schoolchildren. It would be impossible to select the entire school age population of a country, divide them into groups and perform research.
A research group sampling the diversity of flowers in the African savannah could not count every single flower, because it would take many years.
In the education example, the research group could test all of the schools in a city, or select one school in a few different cities. Of course, the process is not that easy, and the researchers must use a battery of statistical techniques, and a good research design, to ensure that this subset is as representative as possible.
The basic question that a researcher should be asking when selecting a sample group is:
How many subjects will I need to complete a viable study, and how will I select them?
For example, if an opinion poll company canvasses opinion by phoning people between 9am and 5pm, they are going to miss most people who are out working, totally invalidating their results. These are called determining factors, and also include poor experiment design, confounding variables and human error.
When sampling, a researcher has two distinct choices:
For example, a study that needs to ask for volunteers is never representative of a population. In such cases, the researcher needs to be aware that they cannot extrapolate the findings to represent an entire population.
A study into heart disease that only looks at middle aged men, between 40 and 60, will say very little about heart disease in women or younger men, although it can always be a basis for future research involving other groups.
However robust the research design, there is always an inherent inaccuracy with any sample-based experiment, due to chance fluctuations and natural variety. Most statistical tests take this into account, and this is why results are judged to a significance level, or given a margin of error.
Sampling is an essential part of most research, and researchers must know how to choose sample groups that are as free from bias as possible, and also be aware of the extent to which they can extrapolate their results back to the general population.