In some situations, the sampling locations are intentionally oversampled due to the higher/lower expected values, providing more information about specific features or characteristics of interest. This sampling strategy, named preferential sampling, is particularly relevant in ecological and environmental studies where researchers may focus on areas with expected high biodiversity, specific habitat characteristics, or other relevant factors. However, this strategy of sampling poses challenges that can lead to inaccurate inferences. Therefore, this study delves into the nuanced exploration of preferential sampling by comparing two prominent models proposed by Diggle et al. (J. R. Stat. Soc. Series C Appl. Stat. 59(2):191–232, 2010. https://doi.org/10.1111/j.1467-9876.2009.00701.x ) and Pati et al. (Biometrika 98(1):35–48, 2011. https://doi.org/10.1093/biomet/asq067 ), hereinafter referred to as Diggle model and Pati model, respectively. The comparative analysis unfolds in two crucial steps: assessing parameter inference efficacy through empirical simulations and establishing theoretical and empirical connections between the models. In the exploration of preferentiality degree inference, the Pati model excelled under strong preferential sampling, while the Diggle model outperformed in scenarios with moderate preferential sampling. An inverse relation between preferentiality degrees emerged, with the Pati model tending to overestimate and the Diggle model to underestimate. In estimating marginal variance, the Pati model outshone the Diggle model under strong preferential sampling, while the Diggle model excelled at moderate preferential sampling. Both models exhibited comparable performance under negative preferentiality. The analysis revealed improved model performance under positive preferentiality, aligning theoretical predictions with empirical observations. The study highlighted the interconnectedness of model parameters, emphasizing precision in estimation.