Statistical inference

Statistical analysis forms the basis of evaluating biomedical research. Medical research has flourished over recent decades leading to continued advancements in understanding of theoretical and applied statistical methods in biomedicine and related fields. This article reviews and provides practical understanding of various theoretical statistics and research methodology and designs. The review deals with measures of central tendency and variability, “confidence intervals, p-values, risk factors, correlation and regression, randomization, sample size, bias and errors, and reporting. Other methodologies and techniques of clinical research and trial designs, such as, blinding, missing data, sensitivity analysis and trial adaptations, are reviewed. The review considers diverse emerging research methods, including Bayesian and other innovative research paradigms, to test medical hypotheses. It reviews methods to exploit digital and other pioneering biomarkers, as well as integrated research and evolution of the biomedical sciences. The focus of this review is to maintain the central role of theory, practice, and methods of medical statistics to research and advances in biomedicine and other related disciplines. The role of technology is to facilitate medical research and provide a framework for scientific inquiry and disciplined pursuit of new knowledge.