“10 Simple Rules for Effective Statistical Practice”

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  • Author: Statistics Views and ASA
  • Date: 23 June 2016
  • Copyright: Image appears courtesy of ClipArt.com

Statistics may be everywhere, but a constant challenge for researchers is that statistics aren’t always understood, calculated or communicated effectively.

Leading statisticians Robert Kass of Carnegie Mellon University; Brian Caffo of Johns Hopkins University; Marie Davidian of North Carolina State University; Xiao-Li Meng of Harvard University; Bin Yu of the University of California Berkeley; and Nancy Reid of the University of Toronto have penned “Ten Simple Rules for Effective Statistical Practice” to help researchers avoid pitfalls of misrepresenting data or formulating hypotheses based on faulty statistical reasoning.

thumbnail image: “10 Simple Rules for Effective Statistical Practice”

The article has been published in PLOS Computational Biology and is available here.

In proposing these guidelines, the authors write, 'A central and common task for us as research investigators is to decipher what our data are able to say about the problems we are trying to solve. Statistics is a language constructed to assist this process, with probability as its grammar. While rudimentary conversations are possible without good command of the language (and are conducted routinely), principled statistical analysis is critical in grappling with many subtle phenomena to ensure that nothing serious will be lost in translation and to increase the likelihood that your research findings will stand the test of time. To achieve full fluency in this mathematically sophisticated language requires years of training and practice, but we hope the Ten Simple Rules laid out here will provide some essential guidelines.'

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