Many analysis techniques are currently available to identify the signaling pathways significantly impacted in a given condition. All these approaches calculate a p-value that aims to quantify the significance of the involvement of a given pathway in the condition under study. These p-values were thought to be related to the likelihood of their respective pathways being involved in the given condition, and to be independent. Here we show that this is not true, and that many pathways are not independent and that can considerably affect each other's p-values through a phenomenon we refer to as "cross-talk". Thus, the significance of a given pathway in a given experiment has to be interpreted in the context of the other pathways that appear to be significant. Using real data, we show that in same cases pathways with significant classical p-values are not biologically meaningful, and that some biologically meaningful pathways with insignificant p-values become significant when the cross-talk effects of other pathways are removed. We show that this phenomenon is related to the amount of common genes between different pathways, affecting the most widely used methods for pathway analysis, and we propose an analysis technique that is able to correct the over-enrichment significance of a pathway when the cross-talk effects of other pathways are removed.