The article Deming, data and observational studies by S. Stanley Young and Alan Karr opens with
Any claim coming from an observational study is most likely to be wrong.
They back up this assertion with data about observational studies later contradicted by prospective studies.
Much has been said lately about the assertion that most published results are false, particularly observational studies in medicine, and I won’t rehash that discussion here. Instead I want to cut to the process Young and Karr propose for improving the quality of observational studies. They summarize their proposal as follows.
The main technical idea is to split the data into two data sets, a modelling data set and a holdout data set. The main operational idea is to require the journal to accept or reject the paper based on an analysis of the modelling data set without knowing the results of applying the methods used for the modelling set on the holdout set and to publish an addendum to the paper giving the results of the analysis of the holdout set.
They then describe an eight-step process in detail. One step is that cleaning the data and dividing it into a modelling set and a holdout set would be done by different people than the modelling and analysis. They then explain why this would lead to more truthful publications.
The holdout set is the key. Both the author and the journal know there is a sword of Damocles over their heads. Both stand to be embarrassed if the holdout set does not support the original claims of the author.
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The full title of the article is Deming, data and observational studies: A process out of control and needing fixing. It appeared in the September 2011 issue of Significance.
Update: The article can be found here.