We introduce a formal framework that allows the calculation of new purely statistical confidence measures for parsing, which are estimated from posterior probability of constituents. These measures allow us to mark each constituent of a parse tree as correct or incorrect. Experimental assessment using the Penn Treebank shows favorable results for the classical confidence evaluation metrics: the CER and the ROC curve. We also present preliminar experiments on application of confidence measures to improve parse trees by automatic constituent relabeling.