Adaptive sampling allows adding sample units in the vicinity of a sample unit selected in the original design, generally used in situations in which the response of interest is clustered or aggregated. (After Lohr, p. 403).
Auxiliary variables are "extra" variables measured in association with the desired response variable that might be used to increase precision of the estimate, by for example developing a modeled relationship between the response variable and auxiliary variables that might be measured at more sites than can be monitored for the primary response variable. Auxiliary variables are used in model assisted surveys to guide the selection of the sample sites. (loosely from Lohr, p. 60-61).
An effect which deprives a statistical result of representativeness by systematically distorting it, as distinct from a random error which may distort on any one occasion but balances out on the average. The bias of an estimator is the difference between its mathematical expectation and the true value it estimates. In the case it is zero, the estimator is said to be unbiased. OECD Source: The International Statistical Institute, "The Oxford Dictionary of Statistical Terms", edited by Yadolah Dodge, Oxford University Press, 2003.
A sample obtained by a biased sampling process, that is to say, a process which incorporates a systematic component of error, as distinct from random error which balances out on the average. Non-random sampling is often, though not inevitably, subject to bias, particularly when entrusted to subjective judgement on the part of human being. OECD Source: A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical.
The complete enumeration of a population or groups at a point in time with respect to well-defined characteristics (ISI). It is possible to conduct stream network censuses in some instances. For example, surveying the entire stream network in small watersheds might be feasible, or some techniques that combine a census of some indicators with calibration (e.g., Hankin and Reeves and related techniques) can be applicable at local spatial scales. Or for some indicators, it might be feasible to enumerate the entire population at key sites in a stream network (e.g., fish counting facilities at watershed outlets). A census is a survey conducted on the full set of observation objects belonging to a given population or universe. OECD Source: Economic Commission for Europe of the United Nations (UNECE), "Terminology on Statistical Metadata", Conference of European Statisticians Statistical Standards and Studies, No. 53, Geneva 2000.
Cluster sampling is a sampling technique where the total population is divided into mutually exclusive and exhaust subgroups (clusters), a sample of the clusters is selected and all elements within a selected subgroup are measured. It is a special case of a two-stage survey design where at the second stage a sample of elements from each cluster (or first stage unit) is taken. The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so analysis is done on a population of clusters (at least in the first stage). In stratified sampling, the analysis is done on elements within strata. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are studied. The main objective of cluster sampling is increase operational efficiency for a given cost. This contrasts with stratified sampling where the main objective is to increase precision. Because of budgetary and timing considerations, most household surveys are based on what are termed cluster samples, that is, cases where the ultimate sample units are chosen in groups of various sizes within only selected parts of the country. OECD Source: Handbook of Household Surveys, Revised Edition, Studies in Methods, Series F, No. 31, United Nations, New York, 1984, para. 4.24. When the basic sampling unit in the population is to be found in groups or clusters, e.g. human beings in households, the sampling is sometimes carried out by selecting a sample of clusters and observing all the members of each selected cluster. This is known as cluster sampling. OECD Source: A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical.
Procedure to remove impact (bias) ofmetric uncertaintyon an indicator estimated cumulative distribution or percentiles estimated for target population and sub-populations.
A variable that takes only a finite number of real values. (e.g., 1, 3, 5 and 1,000). OECD Source: Statistics Canada, Educational Resources, Glossary of Statistical Terms.
Discrete populations consist of populations whose ‘parts’ can be identified and listed, such as the population of lakes or wetlands in a region. Alternatively, stream or road networks are often considered continuous. Continuous populations can be converted into discrete populations by the application of specific rules that break the resource into discrete elements. For example, stream networks could be converted into discrete form by identifying unique reaches defined at the network confluences. In general, we treat stream networks as continuous populations.
A standard form of sample design for industrial inspection purposes. In accordance with the characteristics of a particular plan, two samples are drawn, n1 and n2, and the first sample inspected. The batch can then be accepted or rejected upon the results of this inspection or the second sample be inspected and the decision made upon the combined result. The term has also been used somewhat loosely for what is called multi-phase sampling and the two-stage version of multi-stage sampling. There is a further usage whereby a first sample provides a preliminary estimate of design parameters which govern the size of the second sample to achieve a desired overall result. OECD Source: A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical.
Elements of a population refer to the ‘parts’ that make up the target population. Elements of a discrete population are easy to describe in that they are the individuals that make up the population. Each lake or wetland in a population of lakes or population of wetlands is a population element. For continuous resources, population elements are points on the target resource, e.g., points on a stream network. An important rule in the definition of the population elements is its explicit definition so that members of a field crew can determine whether the site visited is a member of the target population.
One of the individuals which, in the aggregate, compose a population: the smallest unit yielding information which, by suitable aggregation, leads to the population under investigation. OECD Source: A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical.
A term introduced by Matheron (1962) for the study of ‘regionalized variables’; that is, variables supposed to follow some spatial stochastic process (ISI).
The generalized random-tessellation stratified design is an algorithm that creates a spatially balanced selection of sites across 1-dimensional systems (e.g., linear resources such as stream networks), 2-dimensional systems (e.g., areal resources such as forests), or 3-dimensional systems (3-D resources such as oceans or lakes) (after Stevens and Olsen, 2004).
An algorithm for estimating the population totals (e.g., total number of fish) in a population based on a sample, and for estimating its variance. (interpreted from Lohr, p. 196-197) A method of estimating the population total when sampling without replacement from a finite population and when unequal probabilities of selection are used. The estimator is unbiased, linear and can be used with a variety of basic sample designs (ISI; Horvitz and Thompson, 1952).
Component of the monitoring design that defines the process of determining indicator values based on metric values observed at sites during specific temporal periods.
The LNV is an alternate to the HT, or other variance estimators applied to sample surveys. The LNV provides an unbiased estimate of variance if spatial designs (such as GRTS) are used, especially if the response of interest exhibits spatial pattern.
Value resulting from the reduction or processing of measurements taken at a site and a temporal period based on the procedures defined by the response design.
A model is a formalised expression of a theory or the causal situation which is regarded as having generated observed data. OECD Source: A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical.
The steps involved in defining how (response design), where (spatial design) and when (temporal design) field measurements are taken and metrics are calculated so that indicators can be determined (inference design) for study target population and sub-populations.
Nested sampling (or double sampling) involves selecting a sample (first phase) where one set of variables is measured followed by selecting a sub-sample (second phase) of the first sample where an additional second set of variables is measured. In most cases the second set of variables are "expensive" to measure compared to the first set of variables. The purpose of double sampling is to obtain estimates for the second set of variables with lower sampling error by using relationships between variables in the first set and variables in the second set. Note that it is possible to use more than two phases of sampling. Nested sampling is also used to denote two-stage (or multi-stage) sampling where the higher stage units are "nested" within the lower stage units. In two-stage sampling the first stage units (primary sampling units) consist of secondary sampling units at the second stage. A term used in two somewhat different senses: (1) as equivalent to multi-stage sampling because the higher stage units are "nested" in the lower stage units; (2) where the sampling is such that certain units are imbedded in larger units which form part of the whole sample, e.g. the entry-plots of clusters are "nested" in this sense. OECD Source: A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical.
In general, the power of a statistical test of some hypothesis is the probability that it rejects the null hypothesis when that hypothesis is false (ISI).
Adaptive sampling
