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Reporting statistical methods and statistical results in EJN
Author(s) -
Sarter Martin,
Fritschy JeanMarc
Publication year - 2008
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/j.1460-9568.2008.06581.x
Subject(s) - null hypothesis , statistical hypothesis testing , statistical model , variance (accounting) , psychology , computer science , component (thermodynamics) , statistical analysis , selection (genetic algorithm) , data science , management science , statistics , artificial intelligence , accounting , mathematics , physics , economics , business , thermodynamics
EJN publishes research that represents the entire spectrum of the neurosciences. The content and style of the description of the statistical methods and the statistical results in the manuscripts submitted to EJN reflect the diverse customs of the many fields of neuroscientific research. Although EJN embraces these heterogeneous reporting and writing styles, we need to ensure a consistent description of statistical methods and results in our published papers. By definition, a scientific article should permit researchers to reproduce the study described in this article; to this end, an informative and complete description of statistical methods and results is an essential component of an effective scientific report. The aim of this editorial is to provide guidance for the reporting of statistical methods and results. We consulted numerous specialized sources (referenced below) as well as comprehensive guidelines such as Lang & Secic (2006). Finally, the Associate Editors of EJN were consulted. As a result, the present recommendations constitute a component of the EJN Author Guidelines. Statistical methods continue to be debated and to evolve as indicated, for example, by the ongoing discussion about mixed-effect regression models or planned comparisons as alternative approaches to traditional repeated measures ANOVAs and omnibus tests (Lavori, 1990; Gueorguieva & Krystal, 2004; Gonzalez, 2008). The present recommendations should not be viewed as a partisan stance with respect to this or other, sometimes more fundamental, discussions about the use of statistical methods and traditional null hypothesis testing (e.g. Shrout, 1997). The selection of the statistical approaches and the explanation and justification of methods remain the sole responsibilities of the authors. We expect that future ‘Technical Spotlight’ articles will bring some of the new developments in statistics to the readers of EJN.