Statistical hypothesis testing

About: Statistical hypothesis testing is a research topic. Over the lifetime, 19580 publications have been published within this topic receiving 1037815 citations. The topic is also known as: statistical hypothesis testing & confirmatory data analysis.

An exact test for neutrality based on the Ewens sampling distribution

Abstract: Using the Ewens sampling distribution of selectively neutral alleles in a finite population, it is possible to develop an exact test of neutrality by finding the probability of each configuration with the same sample size and observed number of allelic classes. The exact test provides the probability of obtaining a configuration with the same or smaller probability as the observed configuration under the null hypothesis. The results from the exact test may be quite different from those from the Ewens-Watterson test based on the homozygosity in the sample. The advantages and disadvantages of using an exact test in this and other population genetic contexts are discussed.

How should detection probability be incorporated into estimates of relative abundance

Abstract: Determination of the relative abundance of two populations, separated by time or space, is of interest in many ecological situations. We focus on two estimators of relative abundance, which assume that the probability that an individual is detected at least once in the survey is either equal or unequal for the two populations. We present three methods for incorporating the collected information into our inference. The first method, proposed previously, is a traditional hypothesis test for evidence that detection probabilities are unequal. However, we feel that, a priori, it is more likely that detection probabilities are actually different; hence, the burden of proof should be shifted, requiring evidence that detection probabilities are practically equivalent. The second method we present, equivalence testing, is one approach to doing so. Third, we suggest that model averaging could be used by combining the two estimators according to derived model weights. These differing approaches are applied to a mark–recapture experiment on Nuttall's cottontail rabbit (Sylvilagus nuttallii) conducted in central Oregon during 1974 and 1975, which has been previously analyzed by other authors.

HyPhy 2.5-A Customizable Platform for Evolutionary Hypothesis Testing Using Phylogenies.

Abstract: HYpothesis testing using PHYlogenies (HyPhy) is a scriptable, open-source package for fitting a broad range of evolutionary models to multiple sequence alignments, and for conducting subsequent parameter estimation and hypothesis testing, primarily in the maximum likelihood statistical framework. It has become a popular choice for characterizing various aspects of the evolutionary process: natural selection, evolutionary rates, recombination, and coevolution. The 2.5 release (available from www.hyphy.org) includes a completely re-engineered computational core and analysis library that introduces new classes of evolutionary models and statistical tests, delivers substantial performance and stability enhancements, improves usability, streamlines end-to-end analysis workflows, makes it easier to develop custom analyses, and is mostly backward compatible with previous HyPhy releases.

Optimal algorithms theory for robust estimation and prediction

Abstract: This paper deals with the theory of optimal algorithms for problems which cannot be solved exactly. The theory developed allows for the derivation of new and interesting results in parameter estimation and in time series prediction in situations where no reliable statistical hypothesis can be made on the functions and modeling errors involved, but only a bound on them is known, in particular, the derivation of computationally simple optimal algorithms for these two problems is investigated. The practical effectiveness of the algorithms obtained is illustrated by several numerical examples.