Articles avec #statistique - modelisation - plans experimentaux
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Posted on November 2015 by Brian McGill
Teaching a graduate statistics class, I end up as a statistical consultant a lot. One of the questions I get most often is should I treat this as a fixed or a random effect? This topic seems to be shrouded in mystery. Indeed when I came of age statistically in the dark ages (=20 years ago), the main distinction given between a fixed and a random effect was philosophically based: are you measuring a few specific instances of interest in themselves (=fixed) or a few randomly chosen instances interesting only as representatives of a population (=random). This is not a bad approach, and seems clear to me, although I have to confess I have not had great luck teaching this distinction.
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Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4): e1002128. doi:10.1371/journal.pbio.1002128
Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.
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