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Old 21st June 2016, 12:56 PM
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Thumbs up Ten Ways To Avoid Stupid With Statistics [FREE]

Kass, R. E., et al. (2016). "Ten Simple Rules for Effective Statistical Practice." PLoS Comput Biol 12(6): e1004961.
Quote:
Mark Twain popularized the saying, “There are three kinds of lies: lies, damned lies, and statistics.” It is true that data are frequently used selectively to give arguments a false sense of support. Knowingly misusing data or concealing important information about the way data and data summaries have been obtained is, of course, highly unethical. More insidious, however, are the widespread instances of claims made about scientific hypotheses based on well-intentioned yet faulty statistical reasoning. One of our chief aims here has been to emphasize succinctly many of the origins of such problems and ways to avoid the pitfalls.
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.
Among the many articles reporting on the ASA’s statement on p-values, we particularly liked a quote from biostatistician Andrew Vickers in [21]: “Treat statistics as a science, not a recipe.” This is a great candidate for Rule 0.
http://dx.doi.org/10.1371%2Fjournal.pcbi.1004961

http://journals.plos.org/ploscompbio...bi.1004961.PDF

https://www.sciencedaily.com/release...0620191409.htm
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