Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are incon-sistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data

oblige highly associated categories (e.g., flower + pleasant) to share a response key, performance is faster than when less associated categories (e.g., insect + pleasant) share a key. This performance difference implicitly measures differential association of the 2 concepts with the attribute. In 3

-level simulations, we examine various performance and design issues in such protocols, Our analysis leads to a new protocol, MACAW, which uses an RTS-CTS-DS-DATA-ACK message exchange and includes a significantly different backoff algorithm. 1

of the reference stream, the solutions we adopted, and their associated accuracy. Finally, we present evidence that Surge exercises servers in a manner significantly different from other Web server benchmarks.

ix This explanation of significant differences, which applies to the Record of Decision Power Burst Facility and Auxiliary Reactor Area, documents a significant difference in the remedy selected in the record of decision for treatment of the waste contained in the ARA-16 radionuclide tank

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices

attributes of the pictures. The concepts were selected to provide exemplars from several widely studied semantic categories. Sources of naming variance, and mean familiarity and complexity of the exemplars, differed significantly across the set of categories investigated. The potential significance of each

need to equally critically evaluate negative findings. If an author tells us that there was no significant difference between groups, should we believe it? This depends on many issues and raises the question of statistical power. P values describe the risk of making a type I error (�) (that

, matching, energy-based and phase-based methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability and density of the velocity measurements; they show that performance can differ significantly among the techniques we implemented.

Explaining cross-country differences in growth rates requires not only an understanding of the link between growth and public policies, but also an understanding of why countries choose different public policies. This paper shows that ethnic diversity helps explain cross-country differences