Biostatistics stands at the forefront of health sciences, serving as a vital link between raw data and meaningful health insights. This review explores the foundational principles of biostatistics and highlights its diverse applications across clinical trials, epidemiology, genomics, public health policy, environmental health, and healthcare quality assurance. By employing statistical tools such as probability theory, regression models, and machine learning techniques, biostatistics enables researchers to decode complex datasets, evaluate risk factors, and assess the effectiveness of medical and public health interventions. Special attention is given to the role of biostatistics in shaping evidence-based policies, driving precision medicine, and improving health outcomes. The review also addresses the pressing need to integrate biostatistics education at undergraduate and postgraduate levels, noting the gaps in statistical literacy among healthcare professionals. Common errors in statistical interpretation, such as misuse of summary measures and misunderstanding of p-values and confidence intervals, are discussed to emphasize the importance of accurate application. In conclusion, biostatistics not only enhances the scientific rigor of health research but also ensures data-driven decision-making in clinical and public health practice.