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Descriptive statistics sampling is the process of selecting a subset of a population sample for the purpose of estimating the parameters of the population. Though this is normally accomplished by random sampling, there are also systematic sampling methods as well as stratified and cluster sampling techniques. Parameter estimation and hypothesis testing are the most common ways of using descriptive statistics. Parameter estimation seeks to estimate a parameter of the population distribution which describes a property of the sample. Hypothesis testing is a statistical method for evaluating a hypothesized hypothesis, and involves use of the statistical hypothesis test, a procedure that seeks to measure the probability that the observed outcome or result is an accurate reflection of the underlying population distribution.
A data set is a data collection that contains a set of data items made by a sampling of a population or, in the absence of sampling, which provides realizations of a random process. A data set usually consists of observations of the process, where an observation is a realization of the random variable in the process. It is said to be complete when all possible realizations have been collected. A complete data set may be obtained by any kind of sampling based on a probability distribution, and is representative of the underlying population from which the data set was drawn. In a large data set, the observations are assumed to be drawn from a stationary, bounded, and discrete probability distribution. For continuous distributions, the observations in a data set are assumed to be drawn from all possible realizations of the underlying population.
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