Evaluate graduate students' ability to analyze statistical assumptions and common sources of error in differential gene expression (DGE) analysis of RNA‑seq data. Scope includes distributional models (e.g., negative binomial, voom-transformed linear models), overdispersion, independence, heteroscedasticity, normalization and library composition, batch effects, mapping/annotation bias, sample size and replication, and multiple testing. Tasks: diagnose assumption violations (PCA, mean–variance plots, dispersion estimates, SVA), assess impacts on false positives/negatives, and recommend mitigation strategies and appropriate tests.