Abstract
Advanced statistics, often referred to as advanced statistical analysis, pertains to the use of refined statistical methods to examine data, derive insights, and formulate significant conclusions. It advances simple descriptive statistics and investigates connections, patterns, and trends in more detailed manners. Advanced statistics frequently includes multivariate analyses that examine interactions among various variables, Bayesian statistics, causal inference, non-parametric and high-dimensional statistics, estimation theory, optimality criteria, and hypothesis testing. All these areas provide the theoretical foundations for methodologies that are now proving extremely necessary for analyzing data as complex as that relating to biological networks. This paper aims to present possible schemes for integrating advanced statistical methods for inference, analysis and comparison of biological networks, emphasizing the difficulties that these methods, although highly sophisticated, still face, as identified by the study of extensive literature reported in the paper itself.