validáciou

Validáciou, in English validation, is a systematic process used to demonstrate that a product, process, method, or system fulfills its defined requirements and is suitable for its intended use. It relies on evidence, testing, documentation, and traceability to establish credibility, quality, and safety. Validation is often distinguished from verification, where verification assesses conformance to specifications, and validation assesses suitability for the intended purpose.

In Slovak, the noun is validácia and the instrumental form validáciou is used to describe actions performed through validation. The related verb is validovať. The term is widely used across disciplines, ranging from quality assurance and regulatory compliance to scientific research and software development.

Domains and applications

- Data and measurement: Data validation ensures accuracy, consistency, and integrity at data-entry and processing stages, using checks, constraints, and reconciliation procedures. In statistics and psychometrics, validity refers to how well a measurement instrument measures what it intends to measure, including constructs such as construct validity and criterion validity.

- Software and systems: Software validation confirms that a product meets user needs and intended use, often within a formal lifecycle. In regulated industries, process validation ensures that manufacturing processes consistently produce quality outputs, typically requiring documented plans, protocols, qualification, and ongoing change control.

- Research and experimentation: Validation supports generalizability and applicability of findings, with concepts such as internal and external validity affecting causal inferences and real-world relevance.

- AI and machine learning: Model validation assesses performance on unseen data, with techniques like cross-validation helping estimate predictive accuracy and reliability.

Challenges and distinctions

Validation answers whether the right product or process is being built and used appropriately, complementing verification, which answers whether it is built correctly. Challenges include data quality, bias, reproducibility, and regulatory requirements.