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Statistics for the Clinician I: Categorical Variables

  • 0University of New Mexico School of Medicine, Albuquerque, NM, USA.
Seminars in Cardiothoracic and Vascular Anesthesia +

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October 17, 2025

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October 17, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

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.

Keywords:

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