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DOI: 10.1055/s-0044-1796644
Basic Statistics for Radiologists: Part 1—Basic Data Interpretation and Inferential Statistics
Funding None.
Abstract
A systematic approach to statistical analysis is essential for accurate data interpretation and informed decision-making in the rapidly evolving field of radiology. This review provides a comprehensive overview of the fundamental statistical concepts for radiologists and clinicians. The first part of this series introduces foundational elements such as data types, distributions, descriptive and inferential statistics, hypothesis testing, and sampling methods. These are crucial for understanding the underlying structure of research data. The second part of this series delves deeper into advanced topics, including correlation and causality, regression analysis, survival curves, and the analysis of diagnostic tests using contingency tables and receiver operator characteristic (ROC) curves. These tools are vital for evaluating the efficacy of imaging techniques and drawing valid conclusions from clinical studies. As radiology continues to push the boundaries of technology and therapeutic interventions, mastering these statistical principles will empower radiologists to critically assess literature, conduct rigorous research, and contribute to evidence-based practices. Despite the pivotal role of statistics in radiology, formal training in these methodologies is still limited to a certain extent. This primer aims to bridge that gap, providing radiologists with the necessary tools to enhance diagnostic accuracy, optimize patient outcomes, and advance the field through robust research.
Authors' Contributions
All the authors were involved in the procedure, data collection, and manuscript revision.
Publikationsverlauf
Artikel online veröffentlicht:
09. Januar 2025
© 2025. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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