IREF536043false2026-05-13T06:26:45ZSDMX Cross Domain Concept SchemeThe Cross Domain Concept Scheme (starting from v2.0) is created from the set of concepts in the SDMX Glossary, which is an SDMX guideline containing concepts and related definitions that are useful for building and understanding data and metadata exchange arrangements based on SDMX. The Glossary provides definition of terms found in the SDMX Information Model, Data Structure Definitions (DSDs) and Metadata Structure Definitions (MSDs) at the time of the present release. It is recommended as a single entry point to a common SDMX terminology to be used in order to facilitate communication and understanding of the standard.In short, the overall message of the glossary is the following: if a term is used, then its precise meaning should correspond to the SDMX Glossary definition, and any reference to a particular phenomenon described in the SDMX Glossary should use the appropriate term.Version 1.0 of the SDMX Glossary, which replaces the Metadata Common Vocabulary (MCV) published in 2009, was finalised in February 2016. The Cross Domain Concept Scheme v1.0 was created from the MCV concepts.For any question, comment or correction, feel free to contact the SDMX Statistical Working Group (SWG) at the following address: swg@sdmx.org.CONTEXTSampling errors are distinct from errors due to imperfect selection, bias in response or estimation, errors of observation and recording, etc. For probability sampling, the random variation due to sampling can be calculated. For non-probability sampling, random errors cannot be calculated without reference to some kind of model. The totality of sampling errors in all possible samples of the same size generates the sampling distribution of the statistic which is being used to estimate the parent value.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAccuracy; Accuracy - overall; Non-sampling errorSOURCESDMX (2016) (https://sdmx.org/)Sampling errorPart of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.