Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
One of the most difficult and important decisions in power analysis involves specifying an effect size. Researchers frequently employ definitions of small, medium, and large that were proposed by Jacob Cohen. These definitions are problematic for two reasons. First, they are arbitrary, based on non-scientific criteria. Second, they are inconsistent, changing dramatically and illogically as a function of the statistical test a researcher plans to use (e.g., t-test versus regression). These problems may be unknown to many researchers, but they have a huge impact on power analyses. Estimates of the required n may be inappropriately doubled or cut in half. For power analyses to have any meaning, these definitions of effect size should be avoided.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1016/j.tics.2019.12.009 | DOI Listing |
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