Select one or more interests above to see how they connect with biostatistics.

Your Connection: Data

Background: Students from computer science, data science, informatics, or analytics often bring experience organizing large datasets, writing code, identifying patterns, and building computational tools.

Why Biostatistics: Biostatistics gives you the statistical foundation to turn complex health data into reliable evidence. You can move beyond finding patterns to determining whether those patterns are meaningful, reproducible, and useful for improving health.

How Skills Apply: Your experience with programming, databases, algorithms, and data visualization can support the analysis of electronic health records, genomic information, clinical data, and large population-based datasets. You can also help develop responsible AI tools that are both powerful and equitable.

Research Questions You Can Answer: How can electronic health records help us identify patients at risk for disease? How can machine-learning tools support clinical decisions without reinforcing existing health disparities? How can we efficiently analyze genomic and other high-dimensional health data?

Your Connection: Quantitative Problem-Solving

Background: Students from mathematics, statistics, economics, or engineering often come in with strong analytical and problem-solving skills, comfort with abstract reasoning, and experience in modeling or optimization.

Why Biostatistics: Biostatistics is a natural next step because it applies that quantitative foundation to complex, real-world questions about human health, medicine, and biology. Instead of working with purely theoretical models, you get to tackle data that has direct human impact.

How Skills Apply: Your training in proofs, probability, and quantitative modeling transfers into designing experiments, developing statistical methods, and interpreting biomedical data rigorously.

Research Questions You Can Answer: How can we design clinical trials that detect treatment effects efficiently? What is the best way to model the spread of a disease? How can we quantify uncertainty in large-scale genetic studies?

Your Connection: Science and Health

Background: Students from biology, chemistry, neuroscience, medicine, public health, or other scientific fields often bring a strong understanding of living systems, experience with scientific research, and curiosity about the causes and treatment of disease.

Why Biostatistics: Biostatistics allows you to approach scientific questions through data. It provides the tools to design rigorous studies, evaluate evidence, and translate observations into discoveries that can improve medicine and public health.

How Skills Apply: Your understanding of scientific processes helps you ask meaningful research questions, recognize the biological or clinical context behind the data, and collaborate effectively with researchers working in areas such as clinical trials, environmental health, genetics, and genomics.

Research Questions You Can Answer: Does a new treatment improve patient outcomes? How do environmental exposures affect long-term health? Which genetic factors are associated with the development or progression of disease?

Your Connection: Data and Quantitative Problem-Solving

Background: Students with interests in both data and quantitative problem-solving often bring a valuable combination of programming experience, mathematical reasoning, statistical intuition, and comfort working with complex datasets.

Why Biostatistics: Biostatistics connects your ability to build computational tools with the rigorous methods needed to evaluate evidence. It allows you to do more than apply existing algorithms—you can understand their assumptions, improve their performance, and ensure that their results are trustworthy.

How Skills Apply: Your skills in coding, probability, modeling, optimization, and data analysis can be used to develop statistical methods, analyze high-dimensional biomedical data, create risk-prediction models, and build AI-powered clinical tools.

Research Questions You Can Answer: How can we create more accurate models for predicting cancer survival? Which methods work best when a dataset contains thousands of health or genetic variables? How can we make predictive algorithms more reliable and equitable across patient populations?

Your Connection: Data, Science, and Health

Background: Students interested in both data and science often bring experience working with scientific information, interpreting experimental results, using computational tools, or exploring how patterns in data can explain biological and health-related processes.

Why Biostatistics: Biostatistics helps you transform scientific data into evidence that researchers, clinicians, and public health professionals can use. It connects your curiosity about health and biology with the analytical tools needed to drive real-world discovery.

How Skills Apply: Your scientific knowledge helps you understand the meaning behind the data, while your computing and analytical skills help you manage, visualize, and investigate information from clinical trials, electronic health records, genomic studies, and public health research.

Research Questions You Can Answer: What can patient records tell us about the long-term effects of a treatment? How can genomic data reveal pathways involved in disease? Can public health data help us identify communities that face a higher risk during an outbreak?

Your Connection: Quantitative Problem-Solving, Science, and Health

Background: Students drawn to both quantitative reasoning and science often combine a strong interest in biology, medicine, or public health with an aptitude for mathematics, statistical analysis, modeling, or experimental design.

Why Biostatistics: Biostatistics gives you a rigorous way to investigate scientific questions and determine what conclusions the evidence supports. It allows you to use mathematical reasoning in collaborative research that can directly influence health and medicine.

How Skills Apply: Your scientific background helps you frame meaningful questions, while your quantitative skills help you design studies, model complex processes, compare treatments, measure uncertainty, and distinguish meaningful findings from chance variation.

Research Questions You Can Answer: How many participants are needed to determine whether a treatment works? How can we model the relationship between environmental exposures and disease? Which statistical methods can identify meaningful genetic associations while limiting false discoveries?

Your Connection: Data, Quantitative Problem-Solving, Science, and Health

Background: Students interested in data, quantitative reasoning, and science bring together three foundations of biostatistics: computational skill, mathematical thinking, and curiosity about human health, medicine, or biology.

Why Biostatistics: Biostatistics sits directly at the intersection of these interests. It allows you to collaborate on important health challenges, develop rigorous statistical and computational methods, and use real-world data to produce evidence that can improve lives.

How Skills Apply: Your combined strengths can support every stage of the research process—from shaping a scientific question and designing the study to managing complex data, developing new models, quantifying uncertainty, and communicating results to researchers and decision-makers.

Research Questions You Can Answer: How can clinical, genomic, and environmental data be combined to better predict disease risk? Can we develop an AI tool that helps clinicians select treatments while accounting for uncertainty and health disparities? How can new statistical methods turn complex biomedical data into discoveries that are rigorous, reproducible, and equitable?