MS in Biostatistics


The Master of Science program offers advanced training in Biostatistics. The program includes coursework in biostatistics, statistics and one or more biomedical fields. In addition, successful candidates are required to pass a master's theory exam and write a research-based master's thesis. The degree is designed for individuals who anticipate a career as a data analyst in public health or biomedical research or practice and for individuals who plan doctoral work in a public health or biomedical field, but want more methodological training than those PhD programs offer.

Likely Careers

Clinical medicine, epidemiologic studies, biological laboratory and field research, genetics, environmental health, health services, ecology, fisheries and wildlife biology, agriculture, and forestry.


Applicants usually have a degree in mathematics, statistics, or a biological field. All applicants should have the equivalent of 30 or more quarter credits in mathematics and statistics, including linear algebra, probability theory, and approximately 2 years of calculus.

Application Deadline:  Dec 1 - Autumn Quarter Entry


Upon satisfactory completion of the MS in Biostatistics, graduates will be able to:

  • Meet the generic SPH learning objectives for the MS degree;
  • Describe major research study designs and their advantages and limitations;
  • Select and interpret appropriate graphical displays and numerical summaries for both quantitative and categorical data; 
  • Explain the logic of statistical hypothesis tests and confidence intervals;
  • Describe desirable properties of statistical estimation and predictions;
  • Evaluate the properties of commonly used statistical inference procedures;
  • Make appropriate statistical inference to compare one group to a standard, or two or more groups to each other;
  • Use appropriate statistical techniques to perform multiple comparisons, to account for confounding or to gain precision;
  • Use appropriate regression analysis techniques for continuous, binary, count and censored-time-to-event outcomes to analyze independent data from medical and other public health studies;
  • Explain different modeling strategies employed in regression analysis, depending on whether the purpose of the analysis is to develop a predictive model or to make adjusted comparisons;
  • Develop a statistical analysis plan to address the major research questions of a biomedical study based on the data collected and the design of the study; 
  • Determine the sample size needed for a study; and
  • Communicate the aims and results of regression analyses of continuous, binary, count and censored-time-to-event outcomes, to an audience of non-statistician collaborators, including a full interpretation of relevant parameter estimates.