Sreyashi Mondal
Research Mentor: Not Available Not Available
Mentor Department: Not Available, Not Available
Author(s): Not Available
Session: Session 2 (10:00 AM – 10:50 AM)
Presentation Type: Poster 4
Abstract
Hierarchical data are common in biological experiments where multiple observations are often
collected from the same animal across histology, physiology, or behavioral assays. Standard
analysis methods can oversimplify these data by either treating repeated observations as
independent or reducing them to a single summary value per subject. This project uses R-based
statistical workflows to analyze epilepsy datasets in a way that preserves within-subject variation
while accounting for nested experimental structure. By applying mixed-effects approaches, the
analysis improves rigor, reproducibility, and interpretation compared with conventional methods.
More broadly, this work highlights the importance of coding for analyzing hierarchical data
across biological experiments, offering a flexible framework for more accurate and transparent
research.
