Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Performing Security Analysis of Large Scale Power Systems with a Broker-based Computational Grid

In this paper a solution to the on-line power system security analysis is presented For complex networks this analysis requires large computational efforts whereas computation times should be less than few minutes for the information to be useful. Even though many architectures based on conventional parallel and distributed systems have been proposed in literature, none of them is able to furnish useful results for large electrical networks. To address this problem in the paper a distributed Web-based architecture integrating a hierarchical grid system is proposed to implement a computational engine able to execute the power system state equations for large electrical networks. The grid system adopts a resource broker to satisfy the quality of service constraints specified by the user. The resulting framework, deployed on a network of heterogeneous clusters and PCs, is used to compute the contingencies analysis of a realistic electrical grid. The experimental results demonstrate that on-line security analysis can be executed in few minutes even for large network complexity