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|Table of Contents Math Background Introduction to Probability Random Sampling|
|See also: sample space, representative samples, random number generators|
The way samples are selected from a population is very important for
statistical inference, since we use the probability of a sample to infer
the characteristics of the sample population. The most frequently applied
sampling technique is random sampling.
|Definition||If n elements are selected from a population in such a way that every set of n elements in the population has an equal probability of being selected, the n elements are said to be a (simple) random sample.|
The number of possible samples determines the probability of each sample being drawn and therefore is important for the inference on the whole population. Often the listing of all possible events is very tedious or prohibitive in the amount of necessary time and space, e.g. in most lotteries the chances of winning the major prize is 1 out of several million. So we need a more efficient method to determine the number of all samples (combinatorial mathematics).
Random samples are drawn by assigning each of the sample points a number
(from 1 to N) and then selecting k numbers, which are obtained from a random
number generator. Numbers that are selected twice have to be eliminated
and another random number drawn.
Last Update: 2006-Jšn-17