Biostatistics is the application of statistics to the study of biology, and it is vital to many aspects of medical sciences, including genetics, pharmaceuticals, neuroscience, environmental health, and genomics. Biostatistics bridges the gap between living organisms and quantifiable data, and it is the process of analyzing health and biological data. Biostatistics is crucial in clinical trials, epidemiology, and public health. Biostatisticians are data scientists who focus on studies relevant to the advancement of public health, and they use information processing and statistical analysis to help draw guardrails around project scopes and methodology, and inform all the logistical choices around new initiatives, such as test subjects, budgets, and ultimately, their potential for scientific influence on the public health field.

The role of biostatistics in public health is to explain and predict health outcomes, the causation, spread, and control of disease across time and space, and to identify the best way to deploy resources to treat populations. Biostatistics provides scientific, historical data and results, and it gives direction for the future. Biostatistics can help identify the association between an environmental exposure and a health outcome, and it can be used to predict future events using existing data to find patterns and trends. Biostatisticians play pivotal roles in designing studies to ensure enough data and the right kind of information are collected, then they analyze, evaluate, and interpret the results, accounting for variables, biases, and missing data along the way.

In summary, biostatistics is a crucial field in public health that uses statistical analysis to help draw guardrails around project scopes and methodology, and inform all the logistical choices around new initiatives. Biostatistics is used to explain and predict health outcomes, the causation, spread, and control of disease across time and space, and to identify the best way to deploy resources to treat populations. Biostatisticians play pivotal roles in designing studies, analyzing, evaluating, and interpreting the results, accounting for variables, biases, and missing data along the way.