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.

Cliff's Delta Calculator: A non-parametric effect size program for two groups of observations

Abstract: The Cliff´s Delta statistic is an effect size measure that quantifies the amount of difference between two non-parametric variables beyond p-values interpretation. This measure can be understood as a useful complementary analysis for the corresponding hypothesis testing. During the last two decades the use of effect size measures has been strongly encouraged by methodologists and leading institutions of behavioral sciences. The aim of this contribution is to introduce the Cliff´s Delta Calculator software that performs such analysis and offers some interpretation tips. Differences and similarities with the parametric case are analysed and illustrated. The implementation of this free program is fully described and compared with other calculators. Alternative algorithmic approaches are mathematically analysed and a basic linear algebra proof of its equivalence is formally presented. Two worked examples in cognitive psychology are commented. A visual interpretation of Cliff´s Delta is suggested. Availability, installation and applications of the program are presented and discussed.

Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community

Abstract: 1. Analyzing the phylogenetic structure of natural communities may illuminate the processes governing the assembly and coexistence of species. For instance, an association between species co-occurrence in local communities and their phylogenetic proximity may reveal the action of habitat filtering, niche conservatism and/or competitive exclusion. 2. Different methods were recently proposed to test such community-wide phylogenetic patterns, based on the phylogenetic clustering or overdispersion of the species in a local community. This provides a much needed framework for addressing long standing questions in community ecology as well as the recent debate on community neutrality. The testing procedures are based on (i) a metric measuring the association between phylogenetic distance and species co-occurrence, and (ii) a data set randomization algorithm providing the distribution of the metric under a given `null model'. However, the statistical properties of these approaches are not well-established and their reliability must be tested against simulated data sets. 3. This paper reviews metrics and null models used in previous studies. A `locally neutral' subdivided community model is simulated to produce data sets devoid of phylogenetic structure in the spatial distribution of species. Using these data sets, the consistency of Type I error rates of tests based on 10 metrics combined with nine null models is examined. 4. This study shows that most tests can become liberal (i.e. tests rejecting too often the null hypothesis that only neutral processes structured spatially the local community) when the randomization algorithm breaks down a structure in the original data set unrelated to the null hypothesis to test. Hence, when overall species abundances are distributed non-randomly across the phylogeny or when local abundances are spatially autocorrelated, better statistical performances were achieved by randomization algorithms preserving these structural features. The most reliable randomization algorithm consists of permuting species with similar abundances among the tips of the phylogenetic tree. One metric, RPD-DO, also proved to be robust under most simulated conditions using a variety of null models. 5. Synthesis. Given the suboptimal performances of several tests, attention must be paid to the testing procedures used in future studies. Guidelines are provided to help choosing an adequate test.

Likelihood ratio tests for modulation classification

Abstract: We discuss modulation classification (MC) algorithms that are based on the decision theoretic approach, where the MC problem is viewed as a multiple-hypothesis testing problem. In particular, a random-phase AWGN channel is considered and possible solutions to this hypothesis testing problem are reviewed. We present two novel algorithms and we compare their performance with existing ones for a variety of modulation pairs. Simulation results show that these new algorithms can offer a significant performance gain for classification of dense, non-constant envelope constellations.

Bootstrap Methods in Econometrics

Abstract: Although it is common to refer to “the bootstrap,” there are actually a great many dierent bootstrap methods that can be used in econometrics. We emphasize the use of bootstrap methods for inference, particularly hypothesis testing, and we also discuss bootstrap confidence intervals. There are important cases in which bootstrap inference tends to be more accurate than asymptotic inference. However, it is not always easy to generate bootstrap samples in a way that makes bootstrap inference even asymptotically valid.