Statistical population

A statistical population is the complete set of individuals, items, or measurements about which a study seeks to draw conclusions. It may be finite or infinite and defined by time, place, or other criteria.

Statistical sample: A subset of a population selected for statistical analysis. A study usually observes this subset to draw conclusions about the full population.
Probability sampling: A sampling method in which each population unit has a known, nonzero selection probability. Known selection probabilities support estimates of population quantities and their uncertainty.
Census of Population: A national enumeration of residents and households conducted to produce population statistics. It seeks broad coverage of a legally and geographically defined human population.
External validity: The extent to which study findings apply beyond the conditions and participants directly observed. A result generalizes only to populations sufficiently like those supported by the study.
Sampling frame: A list or operational source from which units are selected for a sample. Its coverage determines which members of the intended population can be sampled.
Simple random sample: A sample selected so every subset of a given size has equal probability of selection. It is a basic design for estimating characteristics of a defined population.
Clinical trial: A study that prospectively evaluates health interventions in human participants. Trial results concern a defined population of potential patients, not only enrolled participants.
Nonresponse bias: Bias that occurs when responding units differ systematically from nonresponding units in ways relevant to a study. Even a well-defined population may be poorly represented when selected members do not respond.
Sampling unit: The unit selected at a stage of a sampling design. Population membership must be expressed in units that the design can select.
Sampling bias: Systematic error caused when the sampling process gives some population units unequal or inadequate chances of inclusion. A sample can misrepresent the population when its selection systematically excludes or favors units.

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