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Consumer sensory panels are the gold standard for evaluating food products, yet they often suffer from small sample sizes and sparse, ordinal data. Standard frequentist methods for analyzing this data (e.g. ANOVA, McNemar's test) often violate assumptions or lack statistical power. This thesis evaluates the efficacy of Bayesian Hierarchical Models (BHMs) with cumulative link likelihoods for an
