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Data Dredging Bias

Bias AIO:DataDredgingBias NOT_REVIEWED

A use and interpretation bias where testing many hypotheses in a dataset may yield apparent statistical significance even when results are nonsignificant.

Ontology IRI: https://w3id.org/aio/DataDredgingBias

Ontology Structure & Relationships

Parent Classes

AIO:UseAndInterpretationBias (Use And Interpretation Bias)

Branches

Bias

Annotations & Sources

Synonyms:

Data Dredging exact

Definition Sources:

https://doi.org/10.6028/NIST.SP.1270

Subsets:

https://w3id.org/aio/BiasSubset

Curated AIMech Concepts Grounded to this Class

1 Concept

Curated Concept	Kind	Mapping Relation	Rationale
Single-step parametric maxT inference `AIMech:SingleStepParametricMaxTInference`	Algorithms	related_match	The pinned AIO bias node names apparent significance from testing many hypotheses. This procedure addresses that multiplicity risk for a declared comparison family under a valid joint reference. The algorithm is related to the risk it addresses, not a subtype or equivalent of the bias, and it does not automatically correct unaccounted adaptive selection or every form of data dredging.

Import Provenance & Upstream Synchronization

Source Repo: AIO GitHub

Release Version: aio.json

Commit: 397851c547…

License: CC-BY-4.0

Imported At: 2026-10-10T07:34:10Z

SHA256: 8f741d71408c…

Source File: data/aio/DataDredgingBias.yaml