Validationthrough

Validationthrough is a term used to describe an approach to validation that seeks to establish the credibility of a claim, product, or model by aggregating evidence from multiple independent sources and methods. Rather than relying on a single test, dataset, or stakeholder perspective, validationthrough emphasizes cross-verification and convergence of results to reduce uncertainty.

Practitioners apply it by combining technical validation (unit, integration, and reproducibility tests), empirical validation (field data, experiments with real users), and social validation (stakeholder review, usability and acceptance). The process involves designing studies so that each pillar can confirm or challenge findings, and looking for convergence across lines of evidence.

Determinants of success include predefined success criteria, transparent reporting, and replication. It is commonly used in software engineering, data science, AI safety, and applied social sciences, where complex claims require multifaceted confirmation.

Criticisms include that validationthrough can be resource-intensive and slow, may encourage selective reporting of favorable evidence, and is not a substitute for rigorous single-source validation that tests fundamental assumptions.

See also: triangulation, evidence-based practice, validation, reproducibility.