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Biostatistics is a dynamic and growing field dedicated to the creation of innovative theory and methods for study design, data analysis, and statistical inference, and the promotion of these methods as integral to interdisciplinary research in human health and the life sciences. This is typically achieved by constructing models that capture data from an experiment and then produce predictive data, using induction to draw general results on the basis of specific experiments.

Biostatistical research is interdisciplinary, requiring expertise in Philosophy, Mathematics, Information Science and Biomedical Science. Philosophical questions are raised when we base our conclusions about yet unseen situations upon repeated experimental situations we have seen so far. Our knowledge cannot grow by simple deductive processes based on the logic of certainty. Statistical inference, or inductive reasoning in Statistics, extends the logic of certainty to the uncertain, allowing us to learn from experience. Mathematical tools are used within the inductive process. Information science technologies are used to apply mathematical tools to experimental results and construct general models. Problems and data for Biostatistics come from the Biomedical Sciences. Biostatistics is a meeting point for the most advanced sectors of biomedical research such as Clinical Research, Genomics and Neurosciences. In Clinical Research it is now possible to access increasingly large databases and still collect very detailed information on the clinical state of a single patient, with the goal of identifying prognostic factors of a medical condition and evaluating new therapies. In Genomics and Proteomics an enormous amount of information is now available for the analysis and the understanding of cellular functions, both in normal and pathological conditions. Neuroimaging and other Neurosciences produce data allowing the in vivo examination of functional representation of cerebral maps.

Within each of these realms, Biostatistics plays the role of guaranteeing reliability and credibility of biomedical research. The statistician studies the data and how it can be used for prediction purposes. Accuracy is essential and requires the precise evaluation and interpretation of data or the causal and association mechanisms characterizing the data. A well-designed biomedical study relies on biostatistics for the validity, the cost/benefit ratio and the ethics of any research project.

Link to Knowledge Area: University Center for Statistics in the Biomedical Sciences (CUSSB)

Knowledge Area Leader: Clelia di Serio


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