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10.3758/s13423-017-1266-z http://scihub22266oqcxt.onion/10.3758/s13423-017-1266-z C5862925!5862925!28353065     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Psychon+Bull+Rev 2018 ; 25 (1): 207-18 Nephropedia Template TP {{tp|p=28353065|t=2018. Four reasons to prefer Bayesian analyses over significance testing |pdf=|usr=}}{{28353065}} gab.com Text Four reasons to prefer Bayesian analyses over significance testing JO: Psychon Bull Rev http://www.kidney.de/pget.php?&tw=1&pmid=28353065 #FOAvip Inference using significance testing and Bayes factors is compared and contrasted in five case studies based on real research. The first study illustrates that the methods will often agree, both in motivating researchers to conclude that H1 is supported better than H0, and the other way round, that H0 is better supported than H1. The next four, however, show that the methods will also often disagree. In these cases, the aim of the paper will be to motivate the sensible evidential conclusion, and then see which approach matches those intuitions. Specifically, it is shown that a high-powered non-significant result is consistent with no evidence for H0 over H1 worth mentioning, which a Bayes factor can show, and, conversely, that a low-powered non-significant result is consistent with substantial evidence for H0 over H1, again indicated by Bayesian analyses. The fourth study illustrates that a high-powered significant result may not amount to any evidence for H1 over H0, matching the Bayesian conclusion. Finally, the fifth study illustrates that different theories can be evidentially supported to different degrees by the same data; a fact that P-values cannot reflect but Bayes factors can. It is argued that appropriate conclusions match the Bayesian inferences, but not those based on significance testing, where they disagree. Twit Text FOAVip Four reasons to prefer Bayesian analyses over significance testing ????Psychon Bull Rev http://www.kidney.de/pget.php?&tw=1&pmid=28353065 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28353065 #FOAvip English Wikipedia <ref>{{Cite pmid|28353065}}</ref> Four reasons to prefer Bayesian analyses over significance testing #MMPMID28353065 Dienes Z; Mclatchie N Psychon Bull Rev 2018[]; 25 (1): 207-18 PMID28353065show ga Inference using significance testing and Bayes factors is compared and contrasted in five case studies based on real research. The first study illustrates that the methods will often agree, both in motivating researchers to conclude that H1 is supported better than H0, and the other way round, that H0 is better supported than H1. The next four, however, show that the methods will also often disagree. In these cases, the aim of the paper will be to motivate the sensible evidential conclusion, and then see which approach matches those intuitions. Specifically, it is shown that a high-powered non-significant result is consistent with no evidence for H0 over H1 worth mentioning, which a Bayes factor can show, and, conversely, that a low-powered non-significant result is consistent with substantial evidence for H0 over H1, again indicated by Bayesian analyses. The fourth study illustrates that a high-powered significant result may not amount to any evidence for H1 over H0, matching the Bayesian conclusion. Finally, the fifth study illustrates that different theories can be evidentially supported to different degrees by the same data; a fact that P-values cannot reflect but Bayes factors can. It is argued that appropriate conclusions match the Bayesian inferences, but not those based on significance testing, where they disagree. äFour reasons to prefer Bayesian analyses over significance testing (epmc).pdf DeepDyve Pubget Overpricing