Data Generalizability and Bias
Research gap analysis derived from 3 medicine papers in our local library.
The gap
Most studies suffer from small sample sizes or retrospective designs, limiting their generalizability and introducing potential biases.
Consensus across the literature
The papers collectively establish that current research methodologies are insufficient for broad generalization due to limited sample sizes and retrospective study designs, while leaving open the need for more robust methods.
Research trend
Emerging — attention growing, methods still coalescing.
Supporting evidence — 3 representative gaps
- Unifying to Advance Understanding: Collaborative, Community-Driven and ‘Open’ Approaches for Better Science in Sport (2026) · doi
Data ownership and organizational control of athlete data varies across jurisdictions and is subject to frequent changes in executive management perspectives, creating sustainability risks for collaborative initiatives that require long-term solutions.
Keywords: ownership organizational control athlete varies across jurisdictions subject frequent changes executive management perspectives creating sustainability - Clinical characteristics and short-term outcomes of left ventricular non-compaction cardiomyopathy in neonates (2026) · doi
Due to the small sample size and retrospective design, the results may be subject to potential biases.
Keywords: small sample size retrospective design subject potential biases - The neutrophil-to-lymphocyte ratio is associated with adverse outcomes in patients with anti-neutrophil cytoplasmic antibody-associated vasculitis (2026) · doi
As a single-center retrospective study, the results may be subject to selection bias, which could limit the generalizability of the findings.
Keywords: single center retrospective subject selection bias limit generalizability
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