SSI Course Spotlight: Non-Parametric Statistical Methods for Small Datasets with Dr. Bindu Viswanathan

SSI Course Spotlight: Non-Parametric Statistical Methods for Small Datasets with Dr. Bindu Viswanathan

Instructor Biography

Bindu Viswanathan got her Ph.D. in Biostatistics in 1999 from Emory University. For the next three years, she worked as a Biostatistician at Merck and Novartis, as the statistics lead on Phase III clinical trials in the fields of HIV-AIDS, anxiety/depression and eye diseases. Bindu missed teaching and the academic environment, so she moved back to Emory as research faculty in 2002, teaching, and working on collaborative research projects with the Emory schools of Nursing, Medicine, and Public Health, as well as the CDC and VA healthcare systems. She moved to Austin in 2006 for her husband’s job, and went back to school to earn an M.S. in Conservation Biology from Texas State. Dr Viswanathan started teaching at UT in Spring 2012 and has been here ever since.

What is the main goal of this course?

The main goal of this course is to introduce the participant to all the common statistical tests and methods that are more appropriate to use when dealing with small datasets. Basic statistics courses usually teach parametric methods, and possibly include just one or two non-parametric tests. But a whole world of methods exist for small datasets, that are logical and simple to follow, and that can easily be run using common statistical software. Knowledge of these methods increases the options for the data analyst, who will then be able to select the most appropriate method for data analysis.

What makes you excited about teaching this course?

I often tell my students that at the beginning of their statistics education, they bring an empty toolbox to the class. As they learn more than more techniques, they fill their toolbox with more and more sophisticated tools. The more versatile their collection of tools is, the better they can accomplish the task of data analysis in the most appropriate as well as efficient manner. My course will double the set of tools in a participant’s toolbox! This will now enable them to hone in on the most appropriate technique for analyzing their data.

How much background knowledge or experience in this subject is required to be able to follow the course material?

If the class has some basic statistics knowledge of parametric tests, we can go straight to non-parametric methods. However, that knowledge is not necessary to follow the methods that will be presented here. I will be doing a brief intro to parametric methods in the first meeting, just to get everyone on the same page.

What skills and knowledge can participants expect to acquire by the end of the course?

Knowledge of several simple non-parametric tests that can be used for commonly observed study designs with small datasets. Knowledge of how to run these tests using software, as well as how to work them out manually.

The Department of Statistics and Data Sciences at The University of Texas at Austin is proud to host the 11th annual UT Summer Statistics Institute (SSI) May 21 - 24, 2018. Registration is now open!