In statistics, a sample is a subject chosen from a population
for investigation; a random sample is one chosen by a method involving an
unpredictable component. Random sampling can also refer to taking a number of
independent observations from the same probability distribution, without
involving any real population. The sample usually is not a representative of
the population of people from which it was drawn— this random variation in the
results is termed as sampling error. In the case of random samples,
mathematical theory is available to assess the sampling error. Thus, estimates
obtained from random samples can be accompanied by measures of the uncertainty
associated with the estimate. This can take the form of a standard error, or if
the sample is large enough for the central limit theorem to take effect,
confidence intervals may be calculated.
