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Statistical applications in the biomedical sciences: A review
1 Department of Mathematics, Faculty of Science, Edo State University Uzairue, Nigeria.
2 Department of Biological Sciences, Faculty of Science, Edo State University Uzairue, Nigeria.
Review
International Journal of Science and Research Archive, 2024, 12(02), 1594–1601.
Article DOI: 10.30574/ijsra.2024.12.2.1433
Publication history:
Received on 23 June 2024; revised on 04 August 2024; accepted on 06 August 2024
Abstract:
This study covers applications areas of statistics in biological science. The integration of statistical methods into the biological sciences has revolutionized data analysis, enhancing the accuracy of data analysis and interpretation. This review aims to provide a comprehensive overview of the key statistical techniques used in biomedical research, highlighting their applications, advantages, and limitations. We conducted a systematic literature search across major databases, we focused on studies that have employed descriptive statistics, inferential statistics, regression analysis, multivariate analysis, survival analysis, Bayesian statistics, and machine learning methods in biomedical sciences. Our findings reveal that statistical methods are indispensable in various areas, including genomics, proteomics, epidemiology, ecology, clinical trials, and pharmacology. Each section of this review details the specific applications of these methods, supported by relevant case studies that illustrate their practical implementation and impact. This review underscores the indispensable role of statistics in biological research, providing actionable insights and recommendations for researchers aiming to leverage statistical tools to drive scientific discovery.
Keywords:
Statistical Methods; Biomedical Sciences; Machine Learning; Biostatistics; Multivariate Analysis; Epidemiology
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
