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NurlanLalaev (talk | contribs) I have added the directed acyclic graph approach to explain randomization. Given that randomization is such a big thing in science, this page needs to be improved. I will spend more time on this in the upcoming weeks. That is why I left {{needs update}} mark. |
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==Empirical evidence that randomization makes a difference==
Empirically differences between randomized and non-randomized studies,<ref>{{cite journal| doi=10.1002/14651858.MR000034.pub2|vauthors=Anglemyer A, Horvath HT, Bero L | title=Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials| journal=Cochrane Database Syst Rev|date=April 2014| pmid=24782322| volume=2014|issue=4 | pages=MR000034| pmc=8191367}}</ref>{{Update inline|reason=Updated version https://www.ncbi.nlm.nih.gov/pubmed/38174786|date = February 2024}} and between adequately and inadequately randomized trials have been difficult to detect.<ref>{{cite journal| doi=10.1002/14651858.MR000012.pub3| vauthors=Odgaard-Jensen J, Vist G, etal |title=Randomisation to protect against selection bias in healthcare trials.| journal=Cochrane Database Syst Rev| date=April 2011| volume=2015 |pmid=21491415|pages=MR000012| issue=4| pmc=7150228}}</ref><ref>{{cite journal| doi=10.1186/1745-6215-15-480|vauthors=Howick J, Mebius A |title=In search of justification for the unpredictability paradox| journal=Trials| year=2014| volume=15| pmid=25490908| pages=480| pmc=4295227 |doi-access=free }}</ref>
== Directed acyclic graph (DAG) explanation of randomization ==
Randomization is the cornerstone of many scientific claims. To randomize, means that we can eliminate the confounding factors. Say we study the effect of '''A''' on '''B.''' Yet, there are many unobservables '''U''' that potentially affect '''B''' and confound our estimate of the finding. To explain these kind of issues, statisticians or econometricians nowadays use [[directed acyclic graph]].{{Needs update|date=July 2024}}
==See also==
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