Biostatistics Masters Program

Biostatistics is the science of statistics applied to the analysis of biological or medical data (The American Heritage Medical Dictionary). It is primarily concerned with the proper generation and interpretation of scientific data in the biology, public health and epidemiology, genetics and other health sciences. It is “the discipline concerned with how we ought to make decisions when analyzing biomedical data, [and] formulating explicit rules to compensate both for the fallibility of human intuition in general and for biases in study design in particular.” (Berger and Matthews, 2006) .


Biostatistians play essential roles in designing studies and analyzing data in any interdisciplinary research involving biomedical data. They help formulate the scientific questions to be answered, determine the appropriate sampling techniques, coordinate data collection procedures, and carry out statistical analyses to answer those scientific questions. Biostatisticians with advanced degrees can look forward to excellent career opportunities in government, industry, and academia. The shortage of biostatisticians is noted in Objectives for the Nation and the Seventh Report to the President and Congress on the Status of Health Personnel in the United States ( an excellent article on how to prepare for a career in Biostatistics.

Where are our Graduates

We impact public practice at the local. state, national and international levels

  • Department Of Community Health
  • Academia
  • Centers for Disease Control
  • Private Industry
  • Global health organizations
  • National Institute of Health
  • Non-governmental organizations
  • Click here to read about Biostatistics Alumni careers

Our faculty strive to produce convincing evidence that will lead to improvements in public health, and to develop new and innovative statistical and epidemiologic methods for producing this evidence.  A few areas of current faculty research interests are listed below.

  • Bayesian Statistics
  • Bioinformatics
  • Causal Inference
  • Clinical Trials
  • Cost-Effectiveness Analysis
  • High-Dimensional Data
  • Longitudinal Data
  • Missing Data
  • Reductive Analytics
  • Statistical Computing
  • Statistical Genetics
  • Survival Analysis


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