IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

Could It Be Better to Discard 90% of the Data? A Statistical Paradox

  • Stanley, T. D.
  • Jarrell, Stephen B.
  • Doucouliagos, Hristos

Conventional practice is to draw inferences from all available data and research results, even though there is ample evidence to suggest that empirical literatures suffer from publication selection bias. When a scientific literature is plagued by such bias, a simple discarding of the vast majority of empirical results can actually improve statistical inference and estimation. Simulations demonstrate that, if the majority of researchers, reviewers, and editors use statistical significance as a criterion for reporting or publishing an estimate, discarding 90% of the published findings greatly reduces publication selection bias and is often more efficient than conventional summary statistics. Improving statistical estimation and inference through removing so much data goes against statistical theory and practice; hence, it is paradoxical. We investigate a very simple method to reduce the effects of publication bias and to improve the efficiency of summary estimates of accumulated empirical research results that averages the most precise ten percent of the reported estimates (i.e., ‘Top10’). In the process, the critical importance of precision (the inverse of an estimate’s standard error) as a measure of a study’s quality is brought to light. Reviewers and journal editors should use precision as one objective measure of a study’s quality.

(This abstract was borrowed from another version of this item.)

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://pubs.amstat.org/doi/abs/10.1198/tast.2009.08205
File Function: full text
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by American Statistical Association in its journal The American Statistician.

Volume (Year): 64 (2010)
Issue (Month): 1 ()
Pages: 70-77

as
in new window

Handle: RePEc:bes:amstat:v:64:i:1:y:2010:p:70-77
Contact details of provider: Web page: http://www.amstat.org/publications/tas/index.cfm?fuseaction=main

Order Information: Web: http://www.amstat.org/publications/index.html

No references listed on IDEAS
You can help add them by filling out this form.

This item is featured on the following reading lists or Wikipedia pages:

  1. Meta-Analysis in Economics

When requesting a correction, please mention this item's handle: RePEc:bes:amstat:v:64:i:1:y:2010:p:70-77. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.