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Statistical Analysis in Allergy and Immunology: A Review With Practical Examples
Michal Ordak1, Giovanni Paoletti2,3, Danilo Di Bona4
1Department of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, Medical University of Warsaw, Warsaw, Poland.
Allergy
|August 23, 2025
Summary
This review addresses statistical reporting challenges in allergy and immunology research. It offers best practices for transparent methods, assumption verification, and interpreting statistical significance for improved scientific rigor.
Area of Science:
- Biomedical Research
- Allergy and Immunology
- Biostatistics
Background:
- Statistical analysis is crucial for valid and reproducible biomedical research.
- In allergy and immunology, complex data necessitate clear biostatistical methods for transparency and rigor.
- Inconsistent reporting and misuse of statistical techniques are persistent issues in the field.
Purpose of the Study:
- To provide a structured, practice-oriented overview of key statistical aspects in allergy and immunology research.
- To highlight best practices for transparent reporting, assumption verification, and interpretation of statistical significance.
- To guide researchers, reviewers, and educators in enhancing statistical standards.
Main Methods:
- Review of recent peer-reviewed articles in allergy and immunology.
- Structured overview of essential biostatistical concepts and reporting guidelines.
- Inclusion of practical examples to illustrate sound analytical reasoning.
Main Results:
- Identified common challenges in statistical reporting and application.
- Outlined best practices for transparent statistical methods.
- Emphasized the importance of verifying assumptions and contextualizing statistical significance.
Conclusions:
- Improved statistical reporting and application are essential for advancing allergy and immunology research.
- Adherence to best practices enhances the validity, reproducibility, and clinical relevance of study findings.
- This review serves as a practical guide for improving statistical standards in the field.
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