Topic Abstract: It’s All About the Data

Ji-Hyun Lee, DrPH
Professor & Associate Director for Cancer Quantitative Sciences
Department of Biostatistics


In today’s world, data is the most valuable gold mine. As data becomes a cornerstone of decision-making, statisticians and data scientists are moving from behind-the-scenes roles to the forefront, driving significant advancements in various fields. This presentation will dive into why careers in statistics and data science are not just about numbers in a spreadsheet but are at the heart of making big things happen, effecting meaningful change across different areas. Moreover, to bring these concepts to life, we will showcase the work of a biostatistician at UF Health Cancer Center as a practical example.

Speaker Bio

Dr. Ji-Hyun Lee is a professor of biostatistics in the department of biostatistics at the University of Florida and Associate Director for the Cancer Quantitative Sciences at the University of Florida Health Cancer Center. Her role at the UF Health Cancer Center involves providing strategic leadership and direction, fostering rigorous and integrated research among Cancer Center scientists. Dr. Lee earned her master’s and doctorate in biostatistics from the University of North Carolina at Chapel Hill. Her research focuses on the design and conduct of clinical trials, cluster/group randomized trials, methods for repeated measurements and best statistical practices. Dr. Lee is an elected fellow of the American Statistical Association (ASA) and a certified professional statistician (PStat®) through the ASA. In 2023, Dr. Lee was elected as the 120th President of the ASA. She will serve as the ASA President-Elect in 2024 and as President in 2025.

Florida’s State Academic Standards for Science


Evaluate the impact of biotechnology on the individual, society and the environment, including medical and ethical issues.


Summarize, represent and interpret categorical and numerical data with one and two variables.


Solve real-world problems involving univariate and bivariate categorical data.


Interpret data distributions represented in various ways.


Explain the difference between correlation and causation in the contexts of both numerical and categorical data.


Interpret the margin of error of a mean or percentage from a data set. Interpret the confidence level corresponding to the margin of error.

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