Better P-Curves: Making P-Curve Analysis More Robust To Errors, Fraud, and Ambitious P-Hacking, A Reply To Ulrich and Miller (2015).
When studies examine true effects, they generate right-skewed p-curves, distributions of statistically significant results with more low (.01 s) than high (.04 s) p values. What else can cause a right-skewed p-curve? First, we consider the possibility that researchers report only the smallest signif...
| Publicado en: | Journal of Experimental Psychology. General Vol. 144; no. 6; pp. 1146 - 1153 |
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| Autores principales: | , , |
| Formato: | Artículo |
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American Psychological Association
Dec2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=111424624&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 111424624 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00963445 EPG jtl: Journal of Experimental Psychology. General issn: 00963445 maglogo: N pubinfo: dt: Dec2015 vid: 144 iid: 6 pid: 34 pub: American Psychological Association artinfo: ui: 111424624 10.1037/xge0000104 ppf: 1146 ppct: 7 formats: tig: atl: Better P-Curves: Making P-Curve Analysis More Robust To Errors, Fraud, and Ambitious P-Hacking, A Reply To Ulrich and Miller (2015). aug: au: Simonsohn, Uri Simmons, Joseph P. Nelson, Leif D. affil: University of Pennsylvania University of California, Berkeley su: Statistical errors Computer hacking Skewness (Probability theory) sug: subj: Statistical errors Computer hacking Skewness (Probability theory) keyword: p-curve p-hacking publication bias p-curve p-hacking publication bias ab: When studies examine true effects, they generate right-skewed p-curves, distributions of statistically significant results with more low (.01 s) than high (.04 s) p values. What else can cause a right-skewed p-curve? First, we consider the possibility that researchers report only the smallest significant p value (as conjectured by Ulrich & Miller, 2015), concluding that it is a very uncommon problem. We then consider more common problems, including (a) p-curvers selecting the wrong p values, (b) fake data, (c) honest errors, and (d) ambitiously p-hacked (beyond p < .05) results. We evaluate the impact of these common problems on the validity of p-curve analysis, and provide practical solutions that substantially increase its robustness. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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