Typically, the population is very large, making a census
or a complete enumeration
of all the individuals in the population either impractical or impossible. The sample usually represents a subset of manageable size. Samples are collected and statistics
are calculated from the samples, so that one can make inferences
from the sample to the population.
The sample may be drawn from a population 'without replacement' (i.e. no element can be selected more than once in the same sample), in which case it is a subset
of a population
; or 'with replacement' (i.e. an element may appear multiple times in the one sample), in which case it is a multisubset.
Kinds of samples
A complete sample
is a set of objects from a parent population that includes all
such objects that satisfy a set of well-defined selection criteria.[failed verification]
For example, a complete sample of Australian men taller than 2 m would consist of a list of every
Australian male taller than 2 m. But it wouldn't include German males, or tall Australian females, or people shorter than 2 m. So to compile such a complete sample requires a complete list of the parent population, including data on height, gender, and nationality for each member of that parent population. In the case of human populations, such a complete list is unlikely to exist (the human population being in the billions). But such complete samples are often available in other disciplines, such as the set of players in a major sports league, the birth dates of the members of a parliament, or a complete magnitude-limited list of astronomical objects.
An unbiased (representative) sample
is a set of objects chosen from a complete sample, using a selection process that does not depend on the properties of the objects.
For example, an unbiased sample of Australian men taller than 2 m might consist of a randomly sampled subset of 1% of Australian males taller than 2 m. But one chosen from the electoral register might not be unbiased since, for example, males aged under 18 will not be on the electoral register. In an astronomical context, an unbiased sample might consist of that fraction of a complete sample for which data are available, provided the data availability is not biased by individual source properties.
Mathematical description of random sample
In mathematical terms, given a probability distribution F
, a random sample of length n
may be any positive integer) is a set of realizations of n independent
, identically distributed (iid
) random variables with distribution F
A sample concretely represents the results of n
experiments in which the same quantity is measured. For example, if we want to estimate the average height of members of a particular population, we measure the heights of n
individuals. Each measurement is drawn from the probability distribution F
characterizing the population, so each measured height
is the realization of a random variable
with distribution F
. Note that a set of random variables (i.e., a set of measurable functions) must not be confused with the realizations of these variables (which are the values that these random variables take). In other words,
is a function representing the measurement at the i
-th experiment, and
is the value obtained when making the measurement.
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