Psychometric networks are widely used to analyze multivariate data in psychology and the social sciences. Researchers interpret constructs as networks of variables, focusing on the presence (or absence) and strength of edges—i.e., conditional independencies and partial associations. However, the statistical support for these findings is rarely evaluated, leaving their robustness unclear. Bayesian methods can address this by estimating uncertainty about edges and their weights. We applied this approach to 294 networks from 126 published papers. Results showed inconclusive evidence for one-third of edges, weak evidence for half, and strong evidence for fewer than 20%. Overall, 80% of edges lacked sufficient support to confidently conclude presence or absence. Networks with high relative sample sizes (over 70 observations per edge) were more robust, supporting over half of their edges. These findings suggest that many reported networks rest on limited evidence - this does not mean that results are flawed but rather that they alone do not support strong conclusions. An open-access website, ReBayesed, allows researchers to explore all results and identify robust findings. For details have a look at preprint. The reproducibility archive is available here.