nonidentifiable

Nonidentifiable is an adjective used to describe something that cannot be identified or is not identifiable within a given context. The term is used across fields, including statistics, data privacy, and research ethics, to indicate that identification of a person, parameter, or object is not possible under the stated conditions.

In statistics and related fields, nonidentifiability occurs when multiple parameter values yield the same probability distribution for the observed data, making a unique estimate impossible. Causes include insufficient data, model symmetry, latent variables, or limited variation in the inputs. Consequences include ambiguous inferences and models that cannot be interpreted unambiguously. Addressing nonidentifiability often requires reparameterization, the collection of additional data, or the use of Bayesian priors to achieve practical identifiability.

In data privacy, nonidentifiable (or de-identified) data refers to records from which direct identifiers have been removed so individuals are not readily identifiable. However, even de-identified data can be re-identified through quasi-identifiers or linkage with other datasets. Regulatory approaches distinguish de-identification standards such as HIPAA Safe Harbor and GDPR criteria, and practical methods include anonymization, pseudonymization, k-anonymity, and differential privacy to reduce re-identification risk.

In research practice, the notion of nonidentifiability can influence consent processes and data-sharing decisions when the risk of re-identification or ambiguity about what constitutes identity affects ethics considerations. Overall, nonidentifiability is context-dependent, requiring careful assessment of what counts as identifiable for a given dataset and purpose.