Wigner Surmise For Domain Systems

In random matrix theory, the spacing distribution functions $p^{(n)}(s)$ are well fitted by the Wigner surmise and its generalizations. In this approximation the spacing functions are completely described by the behavior of the exact functions in the limits s->0 and s->infinity. Most non equilibrium systems do not have analytical solutions for the spacing distribution and correlation functions. Because of that, we explore the possibility to use the Wigner surmise approximation in these systems. We found that this approximation provides a first approach to the statistical behavior of complex systems, in particular we use it to find an analytical approximation to the nearest neighbor distribution of the annihilation random walk

Damaging real lives through obstinacy: re-emphasising why significance testing is wrong

This paper reminds readers of the absurdity of statistical significance testing, despite its continued widespread use as a supposed method for analysing numeric data. There have been complaints about the poor quality of research employing significance tests for a hundred years, and repeated calls for researchers to stop using and reporting them. There have even been attempted bans. Many thousands of papers have now been written, in all areas of research, explaining why significance tests do not work. There are too many for all to be cited here. This paper summarises the logical problems as described in over 100 of these prior pieces. It then presents a series of demonstrations showing that significance tests do not work in practice. In fact, they are more likely to produce the wrong answer than a right one. The confused use of significance testing has practical and damaging consequences for people's lives. Ending the use of significance tests is a pressing ethical issue for research. Anyone knowing the problems, as described over one hundred years, who continues to teach, use or publish significance tests is acting unethically, and knowingly risking the damage that ensues

Speeding Up Computer Simulations: The Transition Observable Method

A method is presented which allows for a tremendous speed-up of computer simulations of statistical systems by orders of magnitude. This speed-up is achieved by means of a new observable, while the algorithm of the simulation remains unchanged.Comment: 20 pages, 6 figures Submitted to Phys.Rev.E (August 1999) Replacement due to some minor change

Inference with interference between units in an fMRI experiment of motor inhibition

An experimental unit is an opportunity to randomly apply or withhold a treatment. There is interference between units if the application of the treatment to one unit may also affect other units. In cognitive neuroscience, a common form of experiment presents a sequence of stimuli or requests for cognitive activity at random to each experimental subject and measures biological aspects of brain activity that follow these requests. Each subject is then many experimental units, and interference between units within an experimental subject is likely, in part because the stimuli follow one another quickly and in part because human subjects learn or become experienced or primed or bored as the experiment proceeds. We use a recent fMRI experiment concerned with the inhibition of motor activity to illustrate and further develop recently proposed methodology for inference in the presence of interference. A simulation evaluates the power of competing procedures.Comment: Published by Journal of the American Statistical Association at http://www.tandfonline.com/doi/full/10.1080/01621459.2012.655954 . R package cin (Causal Inference for Neuroscience) implementing the proposed method is freely available on CRAN at https://CRAN.R-project.org/package=ci

Current trends in the cardiovascular clinical trial arena (I)

The existence of effective therapies for most cardiovascular disease states, coupled with increased requirements that potential benefits of new drugs be evaluated on clinical rather than surrogate endpoints, makes it increasingly difficult to substantiate any incremental improvements in efficacy that these new drugs might offer. Compounding the problem is the highly controversial issue of comparing new agents with placebos rather than active pharmaceuticals in drug efficacy trials. Despite the recent consensus that placebos may be used ethically in well-defined, justifiable circumstances, the problem persists, in part because of increased scrutiny by ethics committees but also because of considerable lingering disagreement regarding the propriety and scientific value of placebo-controlled trials (and trials of antihypertensive drugs in particular). The disagreement also substantially affects the most viable alternative to placebo-controlled trials: actively controlled equivalence/noninferiority trials. To a great extent, this situation was prompted by numerous previous trials of this type that were marked by fundamental methodological flaws and consequent false claims, inconsistencies, and potential harm to patients. As the development and use of generic drugs continue to escalate, along with concurrent pressure to control medical costs by substituting less-expensive therapies for established ones, any claim that a new drug, intervention, or therapy is "equivalent" to another should not be accepted without close scrutiny. Adherence to proper methods in conducting studies of equivalence will help investigators to avoid false claims and inconsistencies. These matters will be addressed in the third article of this three-part series