The methods measuring the departure between observation and the model were reviewed. The following statistics were applied on two experimental data sets: ChiSquared, Kolmogorov-Smirnov, Anderson-Darling, Wilks-Shapiro, and Jarque-Bera. Both investigated sets proved not to be normal distributed. The Grubbs’ test identified one outlier and after its removal the normality of the set of 205 chemica...

This paper introduces the two-sample Anderson-Darling (AD) test of goodness of fit as a tool for comparing distributions, response time distributions in particular. We discuss the problematic use of pooling response times across participants, and alternative tests of distributions, the most common being the Kolmogorov-Smirnoff (KS) test. We compare the KS test and the AD test, presenting conclu...

We present an updated analysis of the solar neutrino problem in terms of both Mikheyev-Smirnov-Wolfenstein (MSW) and vacuum neutrino oscillations , with the inclusion of the preliminary data collected by the Su-perKamiokande experiment during 306.3 days of operation. In particular, the observed energy spectrum of the recoil electrons from 8 B neutrino scattering is discussed in detail and is us...

Accurate goodness-of-fit tests for the extreme tails of empirical distributions is a very important issue, relevant in many contexts, including geophysics, insurance, and finance. We have derived exact asymptotic results for a generalization of the large-sample Kolmogorov-Smirnov test, well suited to testing these extreme tails. In passing, we have rederived and made more precise the approximat...

A modified version of the Kolmogorov-Smirnov (KS) test is presented as a tool to assess whether a specified, although arbitrary, probability model is unsuitable to describe the underlying distribution of a set of observations. The KS test computes distances between points of the sample cumulative distribution function and the hypothetical one as absolute differences between them, and then consi...