The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand its functional role within the neural activity. In that case, a Poisson process is the most common model of trial-to-trial variability. However, the variance of the spike count is constrained to be equal to the mean, irrespective of measurement’s duration. Numerous studies have shown that this relationship does not generally hold. Specifically, a majority of electrophysiological recordings show an ``em overdispersion'' effect: Responses that exhibit more inter-trial variability than expected from a Poisson process alone. A model that is particularly well suited to quantify overdispersion is the Negative-Binomial distribution model. This model is largely applied and studied but has only recently been applied to neuroscience. In this paper, we address three main issues. First, we describe how the Negative-Binomial distribution provides a model apt to account for overdispersed spike counts. Second, we quantify the significance of this model for any neurophysiological data by proposing a statistical test, which quantifies the odds that overdispersion could be due to the limited number of repetitions (trials). We apply this test to three neurophysiological tests along the visual pathway. Finally, we compare the performance of this model to the Poisson model on a population decoding task. This shows that more knowledge about the form of dispersion tuning is necessary to have a significant gain, uncovering a possible feature of the neural spiking code.