Carriedsignificantly

Carriedsignificantly is a neologism used in statistics and data science to describe a predictor variable whose significance persists across multiple analyses, models, or data subsets. The term signals that an effect is not only statistically significant in a single model but reliably so when subjected to variation in data or modeling approach, suggesting robust importance.

Definition and usage vary, but the core idea is that the carried significance of the variable remains evident despite changes in sample, cross-validation folds, or alternative specifications. In practice, carriedsignificantly implies that effect size and direction are consistent across analyses, and that the variable contributes to predictive performance beyond a single data split. The term is more common in informal discourse, methodological notes, or exploratory blogs than in formal statistical reporting, and there is no universal standard for its operationalization.

Distinctions from related concepts are important. Carriedsignificantly emphasizes robustness and generalizability of an effect rather than a one-off p-value. It is not a replacement for conventional measures such as p-values, confidence intervals, or cross-validated performance metrics; rather, it is a descriptive label that can accompany results showing consistent significance across resampling or external datasets.

Limitations and reception are mixed. Because carriedsignificantly lacks a formal definition, its interpretation can be ambiguous. When used, it is advisable to accompany it with explicit criteria—such as the number of folds with significant results, consistent effect sizes, and out-of-sample validation—to avoid overstatement.

See also: statistical significance, robustness, cross-validation, replication, effect size.