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Physical Sciences and Mathematics Commons

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Full-Text Articles in Physical Sciences and Mathematics

You Think You’Ve Got Trivials?, Shlomo S. Sawilowsky May 2003

You Think You’Ve Got Trivials?, Shlomo S. Sawilowsky

Journal of Modern Applied Statistical Methods

Effect sizes are important for power analysis and meta-analysis. This has led to a debate on reporting effect sizes for studies that are not statistically significant. Contrary and supportive evidence has been offered on the basis of Monte Carlo methods. In this article, clarifications are given regarding what should be simulated to determine the possible effects of piecemeal publishing trivial effect sizes.


Not All Effects Are Created Equal: A Rejoinder To Sawilowsky, J. Kyle Roberts, Robin K. Henson May 2003

Not All Effects Are Created Equal: A Rejoinder To Sawilowsky, J. Kyle Roberts, Robin K. Henson

Journal of Modern Applied Statistical Methods

In the continuing debate over the use and utility of effect sizes, more discussion often helps to both clarify and syncretize methodological views. Here, further defense is given of Roberts & Henson (2002) in terms of measuring bias in Cohen’s d, and a rejoinder to Sawilowsky (2003) is presented.


A Simulation Study Of Exponential Semiv Arlo Gram Estimation, Edward E. Gbur, Bruce A. Craig, Hao Zhang Apr 2003

A Simulation Study Of Exponential Semiv Arlo Gram Estimation, Edward E. Gbur, Bruce A. Craig, Hao Zhang

Conference on Applied Statistics in Agriculture

Incorporating the spatial structure of data from agricultural field experiments into inference procedures has become an important topic in recent years. As part of a larger project to determine whether or not reliable predictions and estimates can be obtained for sample sizes often encountered in traditional field experimentation, this paper focuses on the small sample estimation of the parameters of the exponential semivariogram model. Simulation studies were conducted for both expanding and fixed domains. The results indicate large sample to sample variation in sample and fitted semivariograms, neither of which may be "close" to the true model. Distributions of individual …