crosscorroboration

Crosscorroboration is the process of confirming the truth or reliability of a claim by seeking multiple independent sources of evidence that converge on the same conclusion. It relies on independent attestations, measurements, documents, or datasets and aims to reduce reliance on a single source or method. True crosscorroboration requires sources that are not themselves mutually dependent.

In journalism, crosscorroboration involves validating information by obtaining additional witnesses, official records, or material from unrelated sources. In science and data analysis, it means replicating findings across independent experiments or applying alternative datasets and methodologies. In law and intelligence, crosscorroboration is used to test hypotheses through diverse, independently verifiable evidence.

Methodologically, practitioners identify relevant independent sources, assess each source’s credibility and independence, compare methods and timelines, and look for convergent conclusions while noting any conflicts. It is important to document levels of confidence and consider potential shared biases or common premises that might undermine independence.

Limitations include the difficulty of achieving true independence, the risk of reinforced bad inferences from correlated sources, and the possibility of false certainty if all sources misinterpret a common premise. Crosscorroboration strengthens evidence but does not guarantee truth, particularly for rare events or complex phenomena.