Assess the ability to critically evaluate the validity (construct, internal, external), reliability (stability, reproducibility), and equity implications (bias, disparate impact, subgroup outcomes, fairness metrics) of learning-analytics predictive models for graduate student retention; includes interpreting performance and calibration metrics, validation strategies (cross-validation, external validation), data quality and measurement issues, causal vs correlational claims, mitigation techniques, stakeholder/ethical considerations, and concrete recommendations for improvement and deployment monitoring.