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10.1111/cts.12178 http://scihub22266oqcxt.onion/10.1111/cts.12178 C5439816!5439816!25043853     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+Transl+Sci 2014 ; 7 (4): 342-6 Nephropedia Template TP {{tp|p=25043853|t=2014. Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias |pdf=|usr=}}{{25043853}} gab.com Text Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias JO: Clin Transl Sci http://www.kidney.de/pget.php?&tw=1&pmid=25043853 #FOAvip A number of commentaries have suggested that large studies are more reliable than smaller studies and there is a growing interest in the analysis of ?big data? that integrates information from many thousands of persons and/or different data sources. We consider a variety of biases that are likely in the era of big data, including sampling error, measurement error, multiple comparisons errors, aggregation error, and errors associated with the systematic exclusion of information. Using examples from epidemiology, health services research, studies on determinants of health, and clinical trials, we conclude that it is necessary to exercise greater caution to be sure that big sample size does not lead to big inferential errors. Despite the advantages of big studies, large sample size can magnify the bias associated with error resulting from sampling or study design. Clin Trans Sci 2014; Volume #: 1?5 Twit Text FOAVip Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias ????Clin Transl Sci http://www.kidney.de/pget.php?&tw=1&pmid=25043853 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25043853 #FOAvip English Wikipedia <ref>{{Cite pmid|25043853}}</ref> Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias #MMPMID25043853 Kaplan RM; Chambers DA; Glasgow RE Clin Transl Sci 2014[Aug]; 7 (4): 342-6 PMID25043853show ga A number of commentaries have suggested that large studies are more reliable than smaller studies and there is a growing interest in the analysis of ?big data? that integrates information from many thousands of persons and/or different data sources. We consider a variety of biases that are likely in the era of big data, including sampling error, measurement error, multiple comparisons errors, aggregation error, and errors associated with the systematic exclusion of information. Using examples from epidemiology, health services research, studies on determinants of health, and clinical trials, we conclude that it is necessary to exercise greater caution to be sure that big sample size does not lead to big inferential errors. Despite the advantages of big studies, large sample size can magnify the bias associated with error resulting from sampling or study design. Clin Trans Sci 2014; Volume #: 1?5 äBig Data and Large Sample Size: A Cautionary Note on the Potential for(epmc).pdf DeepDyve Pubget Overpricing