Assess graduate students' ability to identify, quantify, and mitigate common sources of bias in RNA-seq differential expression workflows (e.g., library size/depth, GC content, gene length, compositional effects, batch effects, and spike‑in variability). Require evaluation and selection of appropriate normalization strategies (RPKM/TPM limitations, DESeq2 size factors, TMM, upper‑quartile, voom, spike‑in/ERCC approaches, and batch correction like ComBat), interpretation of diagnostic plots (MA, PCA, mean‑variance trends), and justification of chosen pipeline with predicted impacts on downstream DE results and false discovery control.