Many tasks in modern probabilistic machine learning and statistics require estimating expectations over posterior distributions. While many algorithms have been developed to approximate these expectations, reliably assessing their performance in practice, in absence of ground truth, remains a significant challenge. In this work, we observe that the well-known k-hat diagnostic for importance sampling (IS) can be unreliable, as it fails to account for the fact that the common self-normalized IS (SNIS) estimator is a ratio. First, we demonstrate that examining separate k-hat statistics for the numerator and denominator can be insufficient. Then, we we propose a new statistic that accounts for the dependence between the estimators in the ratio. In particular, we find that the concept of tail dependence between numerator and denominator weights contains essential information for determining effective performance of the SNIS estimator.