Normalizing flows have demonstrated their ability to learn complex and high-dimensional distributions. However, the behavior of normalizing flow likelihoods are not yet fully understood, particularly when exposed to outlier data, where it has been observed that large likelihoods are often assigned to inputs that are substantially different from the training set. To better understand the likelihood behavior and outlier detection capabilities of normalizing flows, we analyze a more restricted version of the model using synthetic test data from parametric distributions, allowing access to the density of the underlying distribution.