Multiple testing refers to any instance that involves the simultaneous testing of more than one hypothesis. If decisions about the individual hypotheses are based on the unadjusted marginal p-values, then there is typically a large probability that some of the true null hypotheses will be rejected. Unfortunately, such a course of action is still common. In this article, we describe the problem of multiple testing more formally and discuss methods which account for the multiplicity issue. In particular, recent developments based on resampling result in an improved ability to reject false hypotheses compared to classical methods such as Bonferroni.
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.
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.
|This chapter was published in: Steven N. Durlauf & Lawrence E. Blume (ed.) , , pages , 2010, 2nd quarter update.|
|This item is provided by Palgrave Macmillan in its series The New Palgrave Dictionary of Economics with number v:4:year:2010:doi:3826.|
|Contact details of provider:|| Web page: http://www.palgrave-journals.com/ |
|Order Information:|| Web: http://www.dictionaryofeconomics.com/help/faq#_Toc198623697 Email: |
When requesting a correction, please mention this item's handle: RePEc:pal:dofeco:v:4:year:2010:doi:3826. 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: (Sheeja Sanoj)
If references are entirely missing, you can add them using this form.