The course will give an overview of the basic tools for the collection, analysis, and presentation of data in all areas of public health. The topics covered will include descriptive statistics; hypothesis testing; methods for comparison of discrete and continuous data including ANOVA, t-test, correlation, chi-squared analysis, linear and logistic regression, and non-parametric approaches. This course will provide a foundation for the skill-building courses and focused epidemiologic courses.

By the end of the course, participants will be able to:

• Apply numerical and tabular techniques commonly used to characterize and summarize public health data
• Describe basic principles of key concepts including hypothesis testing, type I and type II errors, power, and confidence bounds
• Identify appropriate statistical methods (ANOVA, correlation, linear regression, logistic regression) to be applied in a given research setting, apply these methods, and acknowledge the limitations of those methods
• Evaluate computer output containing statistical procedures and interpret it. Apply learned methods to other courses in public health

There are no required readings but the following textbooks are suggested if needed:

• “Principles of Biostatistics” by Marcello Pagano and Kimberlee Gauvreau, 2nd edition, Brooks/Cole, 2010.
• Less math oriented: 'Fundamentals of Epidemiology and Biostatistics: Combining the basics' by Ray M. Merrill, 1st Edition, Jones & Bartlett Learning, 2013.

Additional material for the course will be provided to the students as needed.

Martina Pavlicova, PhD, MS

Dr. Martina Pavlicova graduated with PhD from the Statistics Department at the Ohio State University, with a primary focus on spatial statistics and statistical issues when analyzing functional magnetic resonance imaging. Currently, her research interests include spatial statistics, statistical issues when analyzing functional magnetic resonance imaging, multiple comparison problems, clinical trials, generalized longitudal mixed effect statistical models, zero-inflated models, and analysis of categorical data. She is also interested in new methods of teaching statistics and biostatistics and developing new teaching approaches for non-statistics master and PhD students. Additionally, Dr. Pavlicova serves as senior biostatistician on several clinical trials in psychiatry.

Sheila Nemeth, PhD

Sheila Nemeth is a doctoral student in Epidemiology with research interests in partner management for sexually transmitted infections and antibiotic resistance. From 2012-2016 she has been a teaching assistant or instructor for Introduction to Biostatistics, Categorical Data Analysis, Applied Regression, and Quantitative Foundations.