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Statistics for the Clinician I: Categorical Variables
Roy Madrid1, Jordan A Buttner1, Mark Shilling2
1University of New Mexico School of Medicine, Albuquerque, NM, USA.
Seminars in Cardiothoracic and Vascular Anesthesia
|October 17, 2025
Summary
This article explains common statistical analyses for categorical variables in clinical research. It covers chi-square tests, Fisher's exact tests, relative risk, and odds ratios for better data interpretation.
Area of Science:
- Clinical Research Statistics
- Biostatistics
- Data Analysis in Medicine
Background:
- Categorical variables are fundamental in clinical research.
- Clinicians require enhanced statistical knowledge to interpret medical literature and improve research.
- Understanding common statistical tests and measures of association is crucial.
Purpose of the Study:
- To introduce clinicians to the uses and analyses of categorical variables.
- To provide a guide for understanding statistical tests and effect sizes in clinical research.
- To enhance critical engagement with medical literature and research design.
Main Methods:
- Description and demonstration of chi-square and Fisher's exact tests.
- Explanation of the logic, interpretation, and limitations of these association tests.
- Introduction to relative risk (RR) and odds ratio (OR) as measures of effect size.
Main Results:
- Chi-square and Fisher's exact tests are key for analyzing categorical data associations.
- Relative risk and odds ratios quantify the effect size in categorical outcome analyses.
- Understanding these methods aids in accurate interpretation of clinical study results.
Conclusions:
- This article equips clinicians with essential statistical tools for categorical data.
- Improved understanding of statistical tests and effect sizes enhances research quality.
- Clinicians can better interpret literature and refine their own research endeavors.
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