Kinematic and force trajectories are often normalized in time, with mean and variance summary statistic trajectories reported. It has been shown elsewhere, for simple one-factor experiments, that statistical testing can be conducted directly on those summary statistic trajectories using Random Field Theory (RFT). This technical note describes how RFT extends to two-factor designs, and how bizar...

The independence assumption, although reasonable when examining cross-sectional data using single-factor experimental designs, is seldom verified by investigators. A Monte Carlo type simulation experiment was designed to examine the relationship between true Types I and II error probabilities in six multiple comparison procedures. Various aspects, such as patterns of means, types of hypotheses,...

91 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Statistical notes for clinical researchers: A one-way repeated measures ANOVA for data w...

Recently, Sakai proposed two methods for determining the topic set size n for a new test collection based on variance estimates from past data: the first method determines the minimum n to ensure high statistical power [22], while the second method determines the minimum n to ensure tight confidence invervals [23]. These methods are based on statistical techniques described by Nagata [15]. Whil...

An Excel sheet with both the data and the results can be downloaded by clicking here. The data correspond to an experiment where 4 new toothpaste formulas were each tested on 6 different patients in order to measure their effect on the whiteness of teeth. All patients had previously used the same toothpaste. Using the ANOVA function of XLSTAT we want to find out if the results differ according ...

decision encoding. Two types of measurements were used for this evaluation. 439 First, we compared the decision SLR coefficients of decision neurons across the three 440 areas (ad, Figure 7A). Moreover, we related the strength of the decision processing of 441 these brain areas to their encoding strength of stimulus intensity, by intensity selective 442 neurons (aint, Figure 7B). We calculated ...