The effect of jitter in sampling on the spectrum and bispectrum of the sampled data has been considered previously. Methods of detecting the presence of jitter in a uniform sampling process and of estimating its variance based on a test statistic calculated from the bispectrum estimates have been proposed. In this work, we demonstrate, by means of Monte-Carlo simulations, how these results can be applied in an actual case. For this purpose, samples taken from a stationary band-limited process in sampling times given by a random jitter process are generated by computer. We then apply the jitter detection and estimation methods that have been developed in previous work and study how their performance depends on signal duration and on jitter variance. We examine the actual simulation results concerning detection probability, estimation bias, and estimation variance in comparison with the theoretical results. This comparison indicates that the bispectrum is a domain where jitter detection and estimation with high performance can be achieved, provided that a signal with sufficiently long duration and high skewness is available.