University of Washington School of Public Health
MPH in Biostatistics
The biostatistics MPH core curriculum provides a breadth of public health knowledge, while additional biostatistics coursework provides in-depth knowledge of biostatistical theory. Biostatistics blends theoretical mathematics and applied data analysis in modern medical and biological research. Biostatisticians work with physicians, epidemiologists, health services researchers, and other scientists to design research studies and to collect and analyze data.
The degree is designed for individuals who anticipate careers as data analysts in public health research or practice and for individuals who plan doctoral work in public health or biomedical field, but who want more methodological training than those PhD programs offer.
At a Glance
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. Applicants must hold a doctoral level degree in another field (e.g., MD, PhD, JD) or be currently working on such a doctoral degree.
Application Deadline: Dec 1 - Autumn Quarter Entry
Upon satisfactory completion of the MPH in Biostatistics, graduates will be able to:
- Meet the generic SPH learning objectives for the MPH degree;
- Meet the Core-Specific Learning Objectives for all MPH students;
- Select and interpret appropriate graphical displays and numerical summaries for both quantitative and categorical data;
- Explain the logic and interpret the results of statistical hypothesis tests and confidence intervals;
- Select appropriate methods for 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 or evaluate 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.