data validity - определение. Что такое data validity
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Что (кто) такое data validity - определение

EXTENT TO WHICH A CONCEPT, CONCLUSION OR MEASUREMENT IS WELL-FOUNDED AND CORRESPONDS ACCURATELY TO THE REAL WORLD
Validity (psychometric); Statistical validity; Reliability and validity; Validity and reliability

Data validation and reconciliation         
  • Normally distributed measurements without bias.
  • Normally distributed measurements with bias.
Random and systematic errors
  • Sensor redundancy arising from multiple sensors of the same quantity at the same time at the same place.
  • Topological redundancy arising from model information, using the mass conservation constraint a=b+c\,\!, for example one can calculate c\,\!, when a\,\! and b\,\! are known.
Sensor and topological redundancy
  • Calculable system, from d\,\! one can compute c\,\!, and knowing a\,\! yields b\,\!.
  • non-calculable system, knowing c\,\! does not give information about a\,\! and b\,\!.
Calculable and non-calculable systems
TECHNOLOGY TO CORRECT MEASUREMENTS IN INDUSTRIAL PROCESSES
User:Robcha/Data Validation and Reconciliation; Data Validation and Reconciliation; Data reconciliation; Industrial process data validation and reconciliation
Industrial process data validation and reconciliation, or more briefly, process data reconciliation (PDR), is a technology that uses process information and mathematical methods in order to automatically ensure data validation and reconciliation by correcting measurements in industrial processes. The use of PDR allows for extracting accurate and reliable information about the state of industry processes from raw measurement data and produces a single consistent set of data representing the most likely process operation.
Data validation         
TECHNICAL PROCESS
Data Checking; Validation rule; Validation routine; Input validation; Validation scheme; Presence check; Data Validation; Cross-reference validation; Validation of data
In computer science, data validation is the process of ensuring data has undergone data cleansing to ensure they have data quality, that is, that they are both correct and useful. It uses routines, often called "validation rules", "validation constraints", or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system.
valid         
WIKIMEDIA DISAMBIGUATION PAGE
ValidIty; Validities; Validly; Scientific validity; Valid; N-valid; N-validity; Validity (disambiguation)
a.
1.
Efficacious, efficient, sound, weighty, powerful, conclusive, logical, cogent, good, just, solid, important, grave, sufficient, strong, substantial.
2.
(Law.) Having legal strength or force, efficacious, executed with the proper formalities, supportable by law or right, good in law.

Википедия

Validity (statistics)

Validity is the main extent to which a concept, conclusion or measurement is well-founded and likely corresponds accurately to the real world. The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.

In psychometrics, validity has a particular application known as test validity: "the degree to which evidence and theory support the interpretations of test scores" ("as entailed by proposed uses of tests").

It is generally accepted that the concept of scientific validity addresses the nature of reality in terms of statistical measures and as such is an epistemological and philosophical issue as well as a question of measurement. The use of the term in logic is narrower, relating to the relationship between the premises and conclusion of an argument. In logic, validity refers to the property of an argument whereby if the premises are true then the truth of the conclusion follows by necessity. The conclusion of an argument is true if the argument is sound, which is to say if the argument is valid and its premises are true. By contrast, "scientific or statistical validity" is not a deductive claim that is necessarily truth preserving, but is an inductive claim that remains true or false in an undecided manner. This is why "scientific or statistical validity" is a claim that is qualified as being either strong or weak in its nature, it is never necessary nor certainly true. This has the effect of making claims of "scientific or statistical validity" open to interpretation as to what, in fact, the facts of the matter mean.

Validity is important because it can help determine what types of tests to use, and help to make sure researchers are using methods that are not only ethical, and cost-effective, but also a method that truly measures the idea or constructs in question.