Before revision:  First, we propose statistical tests to evaluate the quality of p-value  and the cross-batch comparability of any other statistic.
After revision:  First, we propose statistical tests to evaluate the quality of p-value based lasso regression variable selections --- two existing and one we originate. Our method, lassopv, computes p-values for the critical regularization strength at which a predictor starts to contribute to lasso regression. Using null and Geuvadis datasets, we find that lassopv obtains optimal FDC in Bayesian gene networks, whilst existing methods have defective p-values. The FDC concept and tests extend to most network inference scenarios and will guide the design and improvement of new and existing methods. Our novel variable selection method with lasso regression also allows FDC on other datasets and questions, even beyond network inference and the cross-batch comparability of any other statistic.