Randomization test of mean is compuationally inaccessible when the number of groups exceeds two

With the advent of fast computers, the randomization test of mean (also called the permutation test) received some attention in the recent years. Here we show that the randomization test is possible only for two-group design; comparing three groups requires a number of permutations so vast that even three groups of ten participants is beyond the current capabilities of modern computers. Further, we show that the rate of increase in the number of permutation is so large that simply adding one more participant per group to the data results in a computation time increased by at least one order of magnitude (in the three-group design) or more. Hence, the exhaustive randomization test may never be a viable alternative to ANOVAs

A semantic method to prove strong normalization from weak normalization

Deduction modulo is an extension of first-order predicate logic where axioms are replaced by rewrite rules and where many theories, such as arithmetic, simple type theory and some variants of set theory, can be expressed. An important question in deduction modulo is to find a condition of the theories that have the strong normalization property. In a previous paper we proposed a refinement of the notion of model for theories expressed in deduction modulo, in a way allowing not only to prove soundness, but also completeness: a theory has the strong normalization property if and only if it has a model of this form. In this paper, we present how we can use these techniques to prove that all weakly normalizing theories expressed in minimal deduction modulo, are strongly normalizing

Using Mathematica within E-Prime

When programming complex experiments (for example, involving the generation of stimuli online), the traditional experiment programming software are not well equipped. One solution is to give up entirely the use of such software in favor of a low-level programming language. Here we show how E-Prime can be connected to Mathematica so that the easiness and reliability of this software can be preserved while at the same time granting it the full computational power of a high-level programming language. As an example, we show how to generate noisy images with noise proportional to the rate of success of the participants with as few as 12 lines of codes in E-Prime

TLA+ Proofs

TLA+ is a specification language based on standard set theory and temporal logic that has constructs for hierarchical proofs. We describe how to write TLA+ proofs and check them with TLAPS, the TLA+ Proof System. We use Peterson's mutual exclusion algorithm as a simple example to describe the features of TLAPS and show how it and the Toolbox (an IDE for TLA+) help users to manage large, complex proofs.Comment: A shorter version of this article appeared in the proceedings of the conference Formal Methods 2012 (FM 2012, Paris, France, Springer LNCS 7436, pp. 147-154

Embedding Pure Type Systems in the lambda-Pi-calculus modulo

The lambda-Pi-calculus allows to express proofs of minimal predicate logic. It can be extended, in a very simple way, by adding computation rules. This leads to the lambda-Pi-calculus modulo. We show in this paper that this simple extension is surprisingly expressive and, in particular, that all functional Pure Type Systems, such as the system F, or the Calculus of Constructions, can be embedded in it. And, moreover, that this embedding is conservative under termination hypothesis

Analysis of frequency data: The ANOFA framework

Analyses of frequencies are commonly done using a chi-square test. This test, derived from a normal approximation, is deemed generally efficient (controlling type-I error rates fairly well and having good statistical power). However, in the case of factorial designs, it is difficult to decompose a total test statistic into additive interaction effects and main effects. Herein, we present an alternative test based on the $G$ statistic. The test has similar type-I error rates and power as the former one. However, it is based on a total statistic that is naturally decomposed additively into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVAs. We call this set of tools ANOFA (Analysis of Frequency data) to highlight its similarities with ANOVA. We also examine how to render plots of frequencies along with confidence intervals. Finally, quantifying effect sizes and planning statistical power are described under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models

A review of effect sizes and their confidence intervals, Part {I}: The Cohen's d family

Effect sizes and confidence intervals are important statistics to assess the magnitude and the precision of an effect. The various standardized effect sizes can be grouped in three categories depending on the experimental design: measures of the difference between two means (the $d$ family), measures of strength of association (e.$g$., $r$, $R^2$, $\eta ^2$, $\omega ^2$), and risk estimates (e.g., odds ratio, relative risk, phi; Kirk, 1996). Part I of this study reviews the $d$ family, with a special focus on Cohen's $d$ and Hedges' $g$ for two-independent groups and two-repeated measures (or paired samples) designs. The present paper answers questions concerning the $d$ family via Monte Carlo simulations. First, four different denominators are often proposed to standardize the mean difference in a repeated measures design. Which one should be used? Second, the literature proposes several approximations to estimate the standard error. Which one most closely estimates the true standard deviation of the distribution? Lastly, central and noncentral methods have been proposed to construct a confidence interval around $d$. Which method leads to more precise coverage, and how to calculate it? Results suggest that the best way to standardize the effect in both designs is by using the pooled standard deviation in conjunction with a correction factor to unbias $d$. Likewise, the best standard error approximation is given by substituting the gamma function from the true formula by its approximation. Lastly, results from the confidence interval simulations show that, under the normality assumption, the noncentral method is always superior, especially with small sample sizes. However, the central method is equivalent to the noncentral method when $n$ is greater than 20 in each group for a between-group design and when $n$ is greater than 24 pairs of observations for a repeated measures design. A practical guide to apply the findings of this study can be found after the general discussion

Trakt : Uniformiser les types pour automatiser les preuves (démonstration)

National audienceDans un assistant de preuve comme Coq, un même objet mathématique peut souvent être formalisé par différentes structures de données. Par exemple, le type Z des entiers binaires, dans la bibliothèque standard de Coq, représente les entiers relatifs tout comme le type ssrint, des entiers unaires, fourni par la bibliothèque MathComp. En pratique, cette situation familière en programmation est un frein à la preuve formelle automatique. Dans cet article, nous présentons trakt, un outil dont l'objectif est de faciliter l'accès des utilisateurs de Coq aux tactiques d'automatisation, pour la représentation des théories décidables de leur choix. Cet outil construit une formule auxiliaire à partir d'un but utilisateur, et une preuve que cette dernière implique ce but initial. La formule auxiliaire est conçue pour être adaptée aux outils de preuve automatique (lia, SMTCoq, etc). Cet outil est extensible, grâce à une API permettant à l'utilisateur de définir plusieurs natures de plongements dans un jeu de structures de données de référence. Le méta-langage Coq-Elpi, utilisé pour l'implémentation, fournit des facilités bienvenues pour la gestion des lieurs et la mise en oeuvre des parcours de termes en jeu dans ces tactiques