<?xml version='1.0' encoding='UTF-8'?><?xml-stylesheet href="http://www.blogger.com/styles/atom.css" type="text/css"?><feed xmlns='http://www.w3.org/2005/Atom' xmlns:openSearch='http://a9.com/-/spec/opensearchrss/1.0/' xmlns:blogger='http://schemas.google.com/blogger/2008' xmlns:georss='http://www.georss.org/georss' xmlns:gd="http://schemas.google.com/g/2005" xmlns:thr='http://purl.org/syndication/thread/1.0'><id>tag:blogger.com,1999:blog-6880237520211563013</id><updated>2024-09-11T21:23:37.061-07:00</updated><title type='text'>Biases &amp;amp; Confounding in my Worldy Epidemiology</title><subtitle type='html'>Epidemiology as a science is focussed on ironing out the problems of inference and adaptation. Biases and confounding are the cornerstones of the subject and you would expect learning more about the subject would lead you to counter these issues. However, does that happen or do we end up having a more biased view of the world going through all that critical analysis?</subtitle><link rel='http://schemas.google.com/g/2005#feed' type='application/atom+xml' href='http://biased-epidemiologist.blogspot.com/feeds/posts/default'/><link rel='self' type='application/atom+xml' href='http://www.blogger.com/feeds/6880237520211563013/posts/default?redirect=false'/><link rel='alternate' type='text/html' href='http://biased-epidemiologist.blogspot.com/'/><link rel='hub' href='http://pubsubhubbub.appspot.com/'/><author><name>Prabhjot</name><uri>http://www.blogger.com/profile/14882509155374717942</uri><email>noreply@blogger.com</email><gd:image rel='http://schemas.google.com/g/2005#thumbnail' width='16' height='16' src='https://img1.blogblog.com/img/b16-rounded.gif'/></author><generator version='7.00' uri='http://www.blogger.com'>Blogger</generator><openSearch:totalResults>0</openSearch:totalResults><openSearch:startIndex>1</openSearch:startIndex><openSearch:itemsPerPage>25</openSearch:itemsPerPage></feed>