====== Study weakness ======
The term "study [[weakness]]" refers to limitations or flaws in the [[design]], [[execution]], or [[analysis]] of a [[research]] [[study]] that may affect the validity, reliability, or generalizability of its findings.

Here are common types of study weaknesses:

🧪 Methodological Weaknesses
Small sample size: Limits statistical power and increases risk of Type II error.

Lack of control group: Makes it difficult to establish causality.

Selection bias: Non-random participant inclusion can skew results.

Loss to follow-up: Can lead to attrition bias in longitudinal studies.

Short follow-up duration: May not capture long-term effects or outcomes.

Uncontrolled confounders: Variables not accounted for that may influence results.

📊 Data and Analysis Weaknesses
Inadequate statistical analysis: Use of inappropriate or underpowered tests.

Data dredging / p-hacking: Searching for significant results without a clear hypothesis.

Overfitting in models: Particularly in machine learning studies.

Lack of validation cohort: Especially in predictive modeling or biomarker research.

📚 Reporting and Interpretation Issues
Incomplete data reporting: Omitting important variables or methods.

Overgeneralization: Applying results to populations not studied.

Conflict of interest: Funding sources or author affiliations may bias interpretation.

Lack of reproducibility: Insufficient detail to replicate study.

📉 Design-specific Weaknesses
Cross-sectional studies: Cannot establish temporality or causality.

Case reports/series: Anecdotal, with no control group.

Retrospective studies: Prone to recall and selection biases.

Open-label trials: Subject to performance and detection biases.