This study introduces a new, free software tool for analyzing bipartite ecological networks, which calculates a wide range of network indices, visualizes bipartite networks, and generates null models. The software is used to evaluate the sensitivity of 26 network indices to network dimensions, sampling intensity, and singleton observations. The results show that many indices are highly correlated and influenced by network dimensions and connectance. The study re-evaluates five common hypotheses about network properties using 19 pollination networks and three different null models. The findings indicate that while some hypotheses hold up against null models, others are largely explained by network size rather than ecological interrelationships. Notably, the null model patterns for dependence asymmetry and robustness to extinction are opposite to current network paradigms. The analysis and tools provided enable ecologists to compare their findings with null model expectations, distinguishing statistical inevitability from ecological processes. The study highlights the importance of using null models to account for sampling biases and to understand the true ecological significance of network patterns. The results suggest that network indices are strongly influenced by first-order properties such as species abundance and network dimensions, and that second-order properties like connectance and nestedness may be results of these characteristics. The study also shows that some indices, such as interaction strength asymmetry and extinction slopes, are highly correlated with network properties, indicating that these indices may reflect ecological processes rather than random variation. Overall, the study emphasizes the need for careful interpretation of network indices and the use of null models to provide a baseline for expectations based on non-informative network properties.This study introduces a new, free software tool for analyzing bipartite ecological networks, which calculates a wide range of network indices, visualizes bipartite networks, and generates null models. The software is used to evaluate the sensitivity of 26 network indices to network dimensions, sampling intensity, and singleton observations. The results show that many indices are highly correlated and influenced by network dimensions and connectance. The study re-evaluates five common hypotheses about network properties using 19 pollination networks and three different null models. The findings indicate that while some hypotheses hold up against null models, others are largely explained by network size rather than ecological interrelationships. Notably, the null model patterns for dependence asymmetry and robustness to extinction are opposite to current network paradigms. The analysis and tools provided enable ecologists to compare their findings with null model expectations, distinguishing statistical inevitability from ecological processes. The study highlights the importance of using null models to account for sampling biases and to understand the true ecological significance of network patterns. The results suggest that network indices are strongly influenced by first-order properties such as species abundance and network dimensions, and that second-order properties like connectance and nestedness may be results of these characteristics. The study also shows that some indices, such as interaction strength asymmetry and extinction slopes, are highly correlated with network properties, indicating that these indices may reflect ecological processes rather than random variation. Overall, the study emphasizes the need for careful interpretation of network indices and the use of null models to provide a baseline for expectations based on non-informative network properties.