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10.1002/oby.21449 http://scihub22266oqcxt.onion/10.1002/oby.21449 C4817356!4817356!27028280     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Obesity+(Silver+Spring) 2016 ; 24 (4): 781-90 Nephropedia Template TP {{tp|p=27028280|t=2016. Common Scientific and Statistical Errors in Obesity Research |pdf=|usr=}}{{27028280}} gab.com Text Common Scientific and Statistical Errors in Obesity Research JO: Obesity (Silver Spring) http://www.kidney.de/pget.php?&tw=1&pmid=27028280 #FOAvip We identify 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) excessive and undisclosed multiple testing and ?p-value hacking,? 4) mishandling of clustering in cluster randomized trials, 5) misconceptions about nonparametric tests, 6) mishandling of missing data, 7) miscalculation of effect sizes, 8) ignoring regression to the mean, 9) ignoring confirmation bias, and 10) insufficient statistical reporting. We hope that discussion of these errors can improve the quality of obesity research by helping researchers to implement proper statistical practice and to know when to seek the help of a statistician. Twit Text FOAVip Common Scientific and Statistical Errors in Obesity Research ????Obesity (Silver Spring) http://www.kidney.de/pget.php?&tw=1&pmid=27028280 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27028280 #FOAvip English Wikipedia <ref>{{Cite pmid|27028280}}</ref> Common Scientific and Statistical Errors in Obesity Research #MMPMID27028280 George BJ; Beasley TM; Brown AW; Dawson J; Dimova R; Divers J; Goldsby TU; Heo M; Kaiser KA; Keith S; Kim MY; Li P; Mehta T; Oakes JM; Skinner A; Stuart E; Allison DB Obesity (Silver Spring) 2016[Apr]; 24 (4): 781-90 PMID27028280show ga We identify 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) excessive and undisclosed multiple testing and ?p-value hacking,? 4) mishandling of clustering in cluster randomized trials, 5) misconceptions about nonparametric tests, 6) mishandling of missing data, 7) miscalculation of effect sizes, 8) ignoring regression to the mean, 9) ignoring confirmation bias, and 10) insufficient statistical reporting. We hope that discussion of these errors can improve the quality of obesity research by helping researchers to implement proper statistical practice and to know when to seek the help of a statistician. äCommon Scientific and Statistical Errors in Obesity Research (epmc).pdf DeepDyve Pubget Overpricing