This tutorial paper introduces methods for assessing the accuracy of psychological networks and their inferences. Psychological networks represent behavior as a complex interplay of psychological and other components, with nodes as observed variables and edges as statistical relationships. While network estimation methods are well-established, little work has focused on assessing the accuracy of these networks under sampling variation and the stability of centrality indices. The paper proposes bootstrap methods to evaluate the accuracy of network connections, the stability of centrality indices, and differences between network connections and centrality estimates. Two novel statistical methods are introduced: the correlation stability coefficient for assessing centrality index stability and the bootstrapped difference test for comparing edge-weights and centrality indices. Simulation studies are conducted to evaluate the performance of these methods. The R package bootnet is developed to estimate psychological networks and apply the proposed bootstrap methods. The paper also provides a tutorial on using bootnet with a dataset of 359 women with posttraumatic stress disorder. The results show that bootstrapped confidence intervals and stability analyses are essential for interpreting network structures and centrality indices. The paper emphasizes the importance of accuracy in psychological network analysis and provides methods to assess it. The methods are applied to a dataset of PTSD symptoms, revealing that many edge-weights do not significantly differ, and that centrality indices are not always stable. The paper concludes that accuracy assessments are crucial for interpreting psychological network results and that further research is needed to improve the reliability of these methods.This tutorial paper introduces methods for assessing the accuracy of psychological networks and their inferences. Psychological networks represent behavior as a complex interplay of psychological and other components, with nodes as observed variables and edges as statistical relationships. While network estimation methods are well-established, little work has focused on assessing the accuracy of these networks under sampling variation and the stability of centrality indices. The paper proposes bootstrap methods to evaluate the accuracy of network connections, the stability of centrality indices, and differences between network connections and centrality estimates. Two novel statistical methods are introduced: the correlation stability coefficient for assessing centrality index stability and the bootstrapped difference test for comparing edge-weights and centrality indices. Simulation studies are conducted to evaluate the performance of these methods. The R package bootnet is developed to estimate psychological networks and apply the proposed bootstrap methods. The paper also provides a tutorial on using bootnet with a dataset of 359 women with posttraumatic stress disorder. The results show that bootstrapped confidence intervals and stability analyses are essential for interpreting network structures and centrality indices. The paper emphasizes the importance of accuracy in psychological network analysis and provides methods to assess it. The methods are applied to a dataset of PTSD symptoms, revealing that many edge-weights do not significantly differ, and that centrality indices are not always stable. The paper concludes that accuracy assessments are crucial for interpreting psychological network results and that further research is needed to improve the reliability of these methods.