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Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
Validity is the main extent to which a concept, conclusion, or measurement is well-founded and likely corresponds accurately to the real world. [1] [2] The word "valid" is derived from the Latin validus, meaning strong.
Data verification is a process in which different types of data are checked for accuracy and inconsistencies after data migration is done. [1] In some domains it is referred to Source Data Verification (SDV), such as in clinical trials. [2]
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
The definition of M&S validation focuses on the accuracy with which the M&S represents the real-world intended use(s). Determining the degree of M&S accuracy is required because all M&S are approximations of reality, and it is usually critical to determine if the degree of approximation is acceptable for the intended use(s).
Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10] As such, it compares estimates of pre- and post-test probability.
Verification and validation, in engineering or quality management systems, is the act of reviewing, inspecting or testing, in order to establish and document that a product, service or system meets regulatory or technical standards